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MoSiME

Modeling and Simulation in Multidisciplinary Engineering

Principal Investigators: Cecilia Galarza (CONICET) and Guillaume Haiat (CNRS) Co‐principal investigators: Guillermo Artana (CONICET) and Denisse Sciamarella (CNRS) Host institutions: _INSIS, Centre National de la Recherche Scientifique (CNRS) _Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) _ Ministerio de Ciencia, Tecnología e Innovación Productiva (MINCyT)

Proposal duration: 3 years with a possible 2 years extension

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Outline Abstract ..........................................................................................................................3

I) Introduction, motivations and objectives of MoSiME..........................................6 I)1 Historical background and positioning of MoSiME ............................................................. 6 I)2 Current needs of the community............................................................................................ 6 I)3 Selected fields of research ..................................................................................................... 6 I)4 Mission and objectives of MoSiME ....................................................................................... 7

I)4)a Analyses ..............................................................................................................................7 I)4)b Objectives............................................................................................................................8

II) Scientific project.....................................................................................................9 II)1 WP1: Multiscale modeling in solid biomechanics: application to interface problems ...... 9

II)1)a State-of-the-art and objectives ...........................................................................................9 II)1)b Methodology....................................................................................................................11 II)1)c Team members, staff and research environment .............................................................14 II)1)d Risk assessment ...............................................................................................................15 II)1)e Conclusion .......................................................................................................................15

II)2 WP2: Fluid-structure interaction and nature .................................................................... 16 II)2)a State-of-the-art and objectives .........................................................................................16 II)2)b Methodology....................................................................................................................16 II)2)c Team members, staff and research environment .............................................................24 II)2)d) Conclusion......................................................................................................................24

II)3 WP3: Inverse problem ....................................................................................................... 25 II)3)a General formulation .........................................................................................................25 II)3)b Methodology....................................................................................................................26 II)3)c Team members, staff and research environment .............................................................35 II)3)d Conclusion .......................................................................................................................36

III) Organization of the cooperation......................................................................36 III)1 Management ..................................................................................................................... 36

III)1)a Steering committee and management team....................................................................36 III)1)b Scientific committee.......................................................................................................36 III)1)c Evaluation committee .....................................................................................................36

III)2 Cooperation methods........................................................................................................ 37 III)2)a Scientific organization....................................................................................................37 III)2)b Strategy and relation between the workpackages...........................................................37 III)2)c Organization of congress and workshops.......................................................................38 III)2)d Doctoral training and human resources..........................................................................39

III)3 Budget ............................................................................................................................... 39 III)3)a Budget for travel and per diem.......................................................................................39 III)3)b Budget for consumable, workshop and third party services ..........................................40 III)3)c Budget for travel and per diem within Argentina...........................................................40

IV) Conclusion ........................................................................................................40 IV)1 Expected added value ....................................................................................................... 40 IV)2 Long term development of the MoSiME project ............................................................... 41

References ...................................................................................................................41

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Abstract 1. Motivation Too many research developments in mechanics and applied mathematics have long been confined to purely academic works. The MoSiME project aims at providing a strong link between theoretical and numerical developments and industrial applications. To do so, our activities will be centered on modeling and simulation of smart and natural systems behavior, with the overall aim of solving inverse problems encountered in various applications. The researchers and laboratories belonging to the MoSiME project are internationally renowned for their expertise in establishing connections between models and experiments. They are also recognized for their relations to industrial problems and companies from which new theoretical, experimental and computational tools emerge. This remarkable dialogue between industrial needs and engineering sciences is the result of long terms development of the various disciplines in both countries. 2. Main originalities of MoSiME The France-Argentina scientific collaboration network is already very well developed. However, it remains confined to restricted areas in engineering, such as fluid mechanics and there is a strong need of better synergy between the two countries, which will allow to develop the cooperation in this field. At this stage, we believe that this synergy can only be obtained by bringing interdisciplinarity at the heart of this international project, which constitutes a strong originality of MoSiME. The main originalities of MoSiME lie in that i) it includes five full-time researchers permanently based in Giol and ii) it combines pluridisciplinarity together with institutionalized international cooperation. We think that a focused action like the MoSiME project promoting pluridisciplinarity in a fully integrated relatively large project will provide concrete results and contribute to the long-term strengthening of the France-Argentina scientific cooperation. More specifically, MoSiME is interdisciplinar because it aims at taking into account fluid-structure interaction as well as control related problems, with applications in biomechanics and in energy science. 3. Selected fields of research The main research topic selected to promote cooperation within MoSiME is entitled: Modeling and simulation and their applications in multidisciplinary engineering. This is a very wide subject and our strategy consists in identifying different aspects of importance where inverse problems are at stake and to organize the project in order to tackle the related problems by developing specific as well as generic (in particular in worpackage 3) modeling and simulation approaches. MoSiME is composed of three workpackages (WP), which are in strong interaction. MoSiME includes the different research fields below. - Multiscale solid mechanics (WP1). The biomechanical behavior of the bone-implant interface will be considered. Bone tissue is a multiscale medium with non linear mechanical and acoustical properties. - Fluids mechanics (WP2). The fluid-structure interaction occurring in propulsion and vibration mechanisms will be considered. - Energy. Such as smart grids and wind energy. This aspect will be tackled specifically in WP3 but also in WP2, which considers advanced models for wind turbines and state estimation for smart grid. - Inverse problems (WP3). WP3 include four complementary aspects including parameter estimation, data assimilation, image data and fluid flow dynamics coupling and large communication networks for control and remote sensing. Theoretical aspects will be developed in WP3 for the resolution of inverse problems occurring in ultrasound characterization as well on the determination of the constituents of living media through the inversion of multiscale models. 4. Mission and objectives of MoSiME The mission of MoSiME is to gather experts and scholars in order to undertake innovating projects with a high visibility both at a bi-national and international level. The common policy is motivated by the following analyses: (1) The current and future developments in engineering shows the necessity to introduce dedicated models to understand the elementary phenomena influencing the global behavior of complex systems and to provide optimization strategies to the user (2) There is a worldwide trend towards the development of multiscale modeling and simulation in multidisciplinary engineering

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(3) Understanding complex interactions among dynamic systems in order to estimate, predict, or detect faulty behavior of the overall state of the system requires the development of algorithmic tools able to manage large scale models (4) A strong synergy is expected from the coexistence of the various skills of the respective researchers in order to contribute to the resolution of the current and future research problems and applications (5) Franco-Argentinian exchanges of young researchers (post-doc and PhD students) will set the basis for long-term international multidisciplinary collaborations through the training of students and scholars The scientific objectives of MoSiME cover the following main aspects: - the understanding of natural processes as possibly subjected to human intervention. MoSiME includes developments in biomechanics (fluids WP2 and solids WP1). Moreover, some research efforts on Inverse Problems (WP3) will provide better understanding of complex dynamical systems in applications such as energy grid and communication networks. - the development and optimization of industrial processes and products. Strong relationships with industrial applications are considered in MoSiME. Results obtained in WP1 will be useful to surgeons, implant companies and patients following a strong approach. Results obtained in WP2 will be used in phonation for vocal fold surgery. In the domain of propulsion, research can lead to the development of new technologies in transport and flow control. The field of research on wind energy will also be tackled. Results obtained in WP3 will be useful for applications in energy sciences, ambient variable monitoring and flow control. The aim of MoSiME is not to answer collectively to a given and specific scientific question but rather to gather and aggregate in the same laboratory different researchers from diverse background with: _a scientific pertinence and excellence of each researcher in its respective scientific field which are all closely related to the expectation of the call _ a strong willingness of all participants to construct a common laboratory and to collaborate in the coming years, starting research projects together by answering to various calls in both countries and in Europe. 5. Scientific organization The MoSiME project includes two French CNRS researchers from INSIS who take the commitment of staying in Buenos Aires for the entire duration of the project and three Argentinian CONICET researchers. These five researchers will be based in the Polo Científico (Giol) and constitute the kernel of the project, which also involves other Argentinian and French researchers working in “satellite” teams. This organization guarantees the combination of a large Franco-Argentinian research network with permanent researchers working in GIOL. The “satellite” team members will occasionally come to the CAFCI and will realize the associated experiments in their respective laboratories located in France and in Argentina. We describe below some potential future links between the workpackages, which have been considered during discussion but which may not yet be mature enough to be detailed scientifically in a contextualized manner: _Development of model-based inversion procedure in order to retrieve information from the ultrasonic response of the bone-implant interface (WP1-WP3) _Optimal multi-element sensor positioning for the estimation of implant osseointegration (WP1-WP3) _Artificial intelligence approaches to infer bone microstructure from macroscopic measurements (WP1-WP3) _Elasto-dynamic modeling of vocal folds focusing on the influence of the multilayer vocal fold structure on its acoustical response (WP1-WP2) _Development of translational approaches in phonation (WP1-WP2). _Data assimilation and reduced order modelling applied to fluid structure interaction problems (WP2-WP3) Stochastic simulation approaches represent the “red line” of the project since it is considered in the three WP of the project. 6. Organization of congress and workshops The interaction between the researchers and scholars will be allowed by the organization of annual workshops. For each workshop, emphasize will be put on a given WP in order to enhance the scientific coherence of the event. In addition to these annual seminars, smaller monthly seminars will be organized in order to facilitate the scientific communication within the laboratory. The presentation of young researchers will be encouraged. The other planned actions are the following: • Topics will be selected to propose international Summer Schools open to researchers from and outside the project.

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• We plan to participate in the organization of one international conference like for example ICA (International Conference on Acoustics held in Argenina in 2016). 7. Doctoral training and human resources The project will promote Master students from France and Argentina on research subjects related to MoSiME. An important effort will be put towards the training of doctoral and post-doctoral students. In particular, we will put forward fostering co-tutoring PhD programs between the CAFCI and the French satellite. All post-doctoral and doctoral students will have to spend time in France and in Argentina as far as practicable. Exchanges of researchers over periods ranging from one week to three months will be promoted. The idea is to organize the stay of 3 to 5 researchers meeting every day during one to two weeks in Giol. This would promote the elaboration of new models and ideas emerging from deep discussions without time limits and driven by mere curiosity and common endeavour to solve problems.

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I) Introduction, motivations and objectives of MoSiME

I)1 Historical background and positioning of MoSiME The Franco-Argentinian scientific and economic relationships date back from the 19th century. The cooperation in the domain of science and engineering between France and Argentina has now become tremendously important since France is the first partner of Argentina in terms of bilateral scientific cooperation and the third country to welcome Argentinian students, which shows the strength of the relationship between the two countries. The Franco-Argentinian relationship has now gained in maturity such that it is time to work on its international visibility as a powerful platform. In particular, already established relationship in restricted areas exists such as the LIA PMF, dedicated to fluid mechanics, or the UMI IFAECI (Climate Study), dedicated to climate prediction. Note that both entities will have interactions with the MoSiME project, but will also continue the development of their own project independently. Even if the cooperation in the domain of engineering are numerous, there is a strong need of better synergy between the two countries, which will allow to better develop the cooperation in this field. At this stage, we believe that this synergy can only be obtained by bringing interdisciplinarity at the heart of this international project. The main originalities of MoSiME lie in that i) it includes five full-time researchers from various backgrounds in applied mathematics, sold and fluid mechanics in Giol and ii) it combines pluridisciplinarity together with institutionalized international cooperation. We think that a focused action like the MoSiME project promoting pluridisciplinarity in a fully integrated, relatively large project will provide concrete results and contribute to the long-term strengthening of the France-Argentina scientific cooperation. The MoSiME project was first set up during the two informative meetings which took place in 2012 and 2013, where several project members were invited to present an overview of their research directions, now present in the different subjects of MoSiME. A second meeting took place in May 2014 in the Polo Científico with the two project leaders and the two project co-leaders as well as with other participants, in order to set up the solid long-term bases for the project presented in this document.

I)2 Current needs of the community The researchers and laboratories belonging to the MoSiME project are internationally renowned for their expertise in establishing connections between models and experiments. They are also recognized for their relations to industrial problems and companies from which new theoretical, experimental and computational tools emerge. This remarkable dialogue between industrial needs and engineering sciences is the result of long term development of the various disciplines in both countries. On the other hand, Argentinian researchers gathered at or associated to MoSiME belong to the Argentinian tradition of establishing links between mathematical methods and the engineering field. The contribution of Argentinian researchers in this field is acknowledged in the international community. However, applications of such theories to industrial components are not numerous enough and should attract more attention with a view to increase job opportunities for engineers. Promoting the exchanges between France and Argentina will contribute to strengthen the continuum Mathematics–Mechanics–Engineering, in particular revitalizing the theoretical side for France and fostering the connections to engineering applications for the French and Argentinian members.

I)3 Selected fields of research The main research topic selected to promote cooperation within MoSiME is entitled:

Modeling and simulation and their applications in multidisciplinary engineering This subject encompasses the whole expertise of the members of MoSiME and represents the streamline where effective cooperation is expected to take place. This is a very wide subject and our strategy consists in identifying different aspects of importance (described below) where inverse problems are at stake and to

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organize the project in order to tackle the related problems by developing specific as well as generic (in particular Worpackage 3, WP3) modeling and simulation approaches. Several members of the MoSiME have contributed independently to the field in the past ten years but young researchers in both parts have renewed the topic by bringing new insights and perspectives from the theoretical point of view to applications, for instance in biomechanical applications. The thriving rise of young talented scientists shows the fascination exerted by the topic and the promising development that can be expected in the coming twenty years. As such, the topic described above is too large for the MoSiME project so that the scope will be limited to three main areas of theoretical/computational developments and applications, as detailed in section II. More specifically, the different research fields listed below are included in the MoSiME. - Multiscale solid mechanics with topics such as micro-architectured structures, nonlinear dynamics of engineering structures, multiscale modelling of materials facing damage and strain localization. All these aspects will be tackled in WP1 which considers the biomechanical behavior of the bone-implant interface. Bone tissue is a multiscale medium having highly non linear mechanical and acoustical properties. Moreover, microdamage and strain localization are two phenomena of high importance in this context because it influences the implant outcome. - Fluids mechanics : fluid structure interaction, flow control both at low and high Reynolds number, complex flows in fractured porous or confined media, microfluidics. All these phenomena will be tackled in WP2, which considers the fluid-structure interaction occurring in propulsion and vibration mechanisms (as found in the sound source of the phonatory system). Porous media and microfluidic aspects will also be considered in WP2. - Energy systems: such as smart grids and wind energy. This aspect will be tackled specifically in WP3 but also in WP2, which considers advanced models for wind turbines and state estimation for smart grid. - Inverse problems for data assimilation, model and material identification and characterization, or operation of distributed sensor systems, which is the subject of WP3. Note that WP3 include four complementary aspects including parameter estimation, data assimilation, image data and fluid flow dynamics coupling and large communication networks for control and remote sensing. Theoretical aspects will be developed in WP3 for the resolution of inverse problems occurring in ultrasound characterization as well on the determination of the constituents of living media through the inversion of multiscale models.

I)4 Mission and objectives of MoSiME

I)4)a Analyses The mission of MoSiME is to gather experts and scholars in order to undertake innovating projects on important research subjects with a high visibility both at a bi-national and international level. The project is composed of three work-packages (WP) with researchers with diverse backgrounds in engineering sciences including solid and fluid mechanics and applied mathematics. The common policy (see section III for a detailed description) is motivated by the following analyses: (1) The current and future developments in terms of research in (i) material science -including biological tissues- and in (ii) technological development related to micro and nano-smart systems (in particular from the mechanical point of view) shows the absolute necessity to introduce dedicated models and the associated numerical simulations in order to understand the elementary phenomena influencing the global behavior of these complex systems and to provide optimization strategies to the user (2) There is a clear worldwide trend towards the development of multiscale modeling and simulation in multidisciplinary engineering in order to solve problems of increasing complexity (3) Understanding complex interaction among dynamic systems in order to estimate, predict, or detect faulty behavior of the overall state of the system requires the development of algorithmic tools able to manage large scale models (4) A strong synergy is expected from the coexistence of the various skills of the respective researchers in order to contribute to the resolution of the current and future research problems and applications (5) Franco-Argentinian exchanges of young researchers (post-doc, PhD students) will set the basis for long-term international collaborations through the training of students and scholars performing challenging research activities at the interface of various disciplines.

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I)4)b Objectives The scientific objectives of MoSiME cover the following main aspects: - the understanding of natural processes as possibly subjected to human intervention. MoSiME includes important developments in the domain of biomechanics (fluids WP2 and solids WP1). The aim of these two WP is to provide a better understanding of a variety of natural processes, in particular in the domain of bone biomechanics (WP1) and in the domain of nature-inspired fluid-structure interaction (WP2). Mechanical and civil engineering are dedicated to applications of mechanics. Symmetrically, biomechanical engineering focuses on clinical advances as well as progresses in the domain of bioengineering. In this context, the MoSiME project undertakes finalised research relying on basic science approaches in order to identify key concepts at work in nature. Basic as well as applied, this research is multidisciplinary by nature and related to life sciences through collaborations (e.g. CINEOT and APHP). Moreover, part of the research efforts on Inverse Problems (WP3) will provide better understanding of complex dynamical systems encountered in technological applications. The aim of WP3 is to attack specific problems in fluid dynamics, energy grid (transportation and distribution), and communication networks. Bearing on the particular aspects of each field, the general goal is to analyze the system behavior using observations obtained from its ouput. The purpose of the analysis may be resources optimization (e.g. energy systems), performance optimization (e.g. communication networks), forecasting (e.g. fluid dynamical systems). The algorithmic techniques to develop require strong mathematical foundations and the results obtained will enable the group to manage large scale complex dynamic systems. - the development and optimization of industrial processes and products. Strong relationships with industrial applications are considered in MoSiME. In particular, results obtained in WP1 will be useful to: _dental and orthopedic surgeons because it will help them conceive more reliable surgical strategies and to predict and reduce implant failures by bringing quantification in a field where empirical approaches are dominant. _implant companies to conceive more reliable and durable implants. Note in particular the presence in the project of SIDDHI, an implant company which is very interested in the results obtained in WP1. _patients by helping understanding the mechanisms at work in their own bone and adapt their behaviour after implant surgery. Moreover, a strong translational aspect is present in WP1 since a start-up aiming at commercializing a medical device developed in MoSiME is already incubated in France and will be a partner of the project. The start-up is a candidate for the call run but the French Embassy in Argentina, BPI and the MINCyT for the cooperation between companies in research and development between France and Argentina. Results obtained in WP2 will be used in a wide range of fields. In the domain of phonation, results will be useful to improve vocal fold models and to apply the extracted principles to voice processing/synthesis, vocal fold surgery, and clinical applications. Jet pulsating systems for flow control inspired in the principles of voice production are also foreseen. In the domain of propulsion, research can lead to the development of new technologies in transport and flow control. The field of research on wind energy will also be tackled, with important applications in industry. Finally, results obtained in WP3 will be useful for applications in energy sciences, ambient variable monitoring and flow control. The pursuit of the previous scientific objectives will be favored by the research incentives brought by the project. New opportunities are sought for in order to: • Promote connections between theoretical, computational and experimental works. Both France and Argentinian researchers of MoSiME, each in its way, combine these approaches but, especially in the last ten years, in an overly compartmentalized way. Linking rigorous formulations of practical problems to simulations and design of systems will be a strong incentive of the projects thanks to the implication of researchers acquainted to the experimental/theoretical/computational vision of the field. • Promote young French and Argentinian students and researchers. The MoSiME project can be proud of its new generation of young researchers remarkably talented in combining sophisticated theoretical, experimental and computational tools for their research projects. The same holds for the young scientists involved in MoSiME, which will help supporting their projects and stimulating the cooperation by giving common objectives to all project members. • Promote pluridisciplinarity. The MoSiME project is composed of researchers of various backgrounds, from fluid and solid mechanics, to applied mathematics and including electronics as well as bioengineers and medical doctors, which is the only solution to understand the phenomena at stake in the complex systems considered.

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The scientific project is organized into three main directions described in section II, for which two leaders were selected, one in each country. Their task consists in promoting, organizing and reporting on the actions related to their specific topic. The choice of these outstanding researchers was made with regards to their knowledge of the research field, their involvement in previous French–Argentinian actions, and their willingness and enthusiasm to promote the MoSiME project. The practical cooperation methods based on workshops, research exchange, Summer Schools, etc. are presented in section III as well as the connections between the three WP. The aim of MoSiME is not to answer collectively to a given and specific scientific question but rather to gather and aggregate in the same laboratory different researchers from diverse background with: _a scientific pertinence and excellence of each researcher in its respective scientific field which are all closely related to the expectation of the call _ a strong willingness of all participants to construct a common laboratory _a strong willingness of all participants to collaborate in the coming years and to start real research projects together by answering to various calls in both countries and in Europe Subsection III)2)b describes some potential future links between the workpackages, which have been considered during discussion. Although we believe that these interactions are extremely promising, these interdisciplinary collaborations have only been discussed recently and are not yet mature enough to be detailed scientifically in a contextualized manner. These actions will be done in the beginning of the project. Instead, the MoSiME project proposes a consortium of highly competent researchers in their domain and with a strong desire to collaborate in the future. Although clear scientific directions are given in the project, we should remain open to the involvement of additional researchers not aware of the project at this stage. We have preferred to focus the scientific project on specific targets rather than stating large topics but the MoSiME project will also remain open to new research subjects, related to the main objectives that could emerge in the course of the project.

II) Scientific project

II)1 WP1: Multiscale modeling in solid biomechanics: application to interface problems

French side leader: Guillaume Haiat Argentinian side leader: Adrian Cisilino

The research activities of WP1 are mostly centered on biomechanical modeling and of the associated numerical simulation aiming at understanding the main phenomena governing the mechanical and acoustical behavior of living tissues and in particular of the bone-implant interface properties. One of the main characteristics of bone tissue around an endosseous implant is to adapt its structure to mechanical loading in order to accommodate the presence of the implant through mecanosensors. A particular effort will be put towards modeling of the aforementioned phenomena, which constitutes one of the originality of WP1. Two principal research axes may be considered including: _Mechanics of bone remodeling phenomena integrating multiscale and multitime aspects. _Characterization of living tissue using quantitative ultrasonic techniques as well as multiscale approaches coupled to inversion procedures. As described below, various interactions exist between the two aforementioned aspects which will guarantee that the team works in a coherent and motivating manner. Moreover, the multidisciplinary aspect of biomechanical engineering will induce strong needs of collaboration with the other WP.

II)1)a State-of-the-art and objectives Context. Population ageing explains the increasing interest in studying the osteo-articular system. Implanting biomaterials in bone to restore the organ functionality has allowed considerable progress in orthopaedic and dental surgery. Despite routine clinical use, implant failures, which may have dramatic consequences, still occur and are difficult to anticipate. Surgeons introduce biomaterials in a bone cavity formed by drilling, damaging bone tissue but also stimulating bone healing. To do so, non standardized empirical methods are

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often employed. During bone healing, ‘low level’ interfacial micro-motions may stimulate bone remodelling but fibrous tissue develops instead of bone in the case of ‘excessive’ relative movement. Bone is a complex multiscale composite material. At the scale of several hundred nm, bone is composed of hydroxyapatite and water. At the scale of 1–10 µm, bone is made of collagen fibres. At the scale of several hundred µm, the microstructure depends on bone type. Bone can adapt its structure to mechanical stresses (remodelling phenomena[1]), which induce changes of bone properties to accommodate the implant. During bone formation (typically a few months), the spatio-temporal variations of the bone properties are heterogeneous and rely on complex signalling pathways which remain unclear. The main steps of bone regeneration are: i) deposition of an extracellular matrix, ii) mineralization and iii) remodelling of woven to mature bone, a process affected by fluid flow, chemical pathways and stress state[2]. The biomechanical properties of the bone-implant interface are the essential data determining the implant stability. A good quality of bone healing leads to direct contact between bone and the implant over a large proportion of the implant surface. However, the biomechanical (and adhesive) properties of newly formed bone tissue remain unknown. Despite these difficulties, implants design has been driven by empirical approaches which are limited to understanding the determinants of osseo-integration. A better understanding of the biomechanical properties of the bone-implant interface may lead to accurate predictions of implant surgical failure[3], limiting painful and expensive additional surgical interventions. State of the art. Much work has been done in the domain of bone biomechanical modelling but relatively few studies have focused on the understanding of the multiscale modelling of evolution of the bone-implant interface, which constitutes the main obstacle to improving surgical techniques. The main difficulties are related to: i) the complex multiscale nature of bone, ii) the time evolving bone properties due to remodelling phenomena and iii) the presence of an interface. At the organ level, models are often continuum based[4]. At the tissue scale, models account for the bone microarchitecture[5] and collagen fibres [6]. At the cellular level [7], partial differential equations describe cellular interactions[8] and molecular mechanics approach are used to model the collagen fibres[9]. These models are usually developed independently, which makes their interpretation limited. A key point lies in the coupling of in vivo experiments with simulation, which should be done through benchmarking of the prognostic capabilities of the numerical tools. Interestingly, continuum micromechanics approaches allow the modelling of bone anisotropic elastic behaviour[10, 11], accounting for bone poroelasticity[12]. Coupling 3-D reconstructions obtained from imaging techniques with numerical simulation tools is a powerful approach to retrieve realistic bone properties[13]. A critical issue consists in the anisotropy and heterogeneity[13-15] of bone tissue. Recently, a homogenization model was developed allowing to bridge the nano-scale up to the organ scale using data obtained from synchrotron radiation µCT[16, 17]. However, the existing multiscale models do not account for the temporal bone evolution due to remodelling phenomena, which are determinant for implant stability. It remains difficult to assess implant stability in vivo. The resolution of X-ray micro-computed tomography (µCT) is limited due to distortion effects. Biomechanical methods [18, 19] are commercialized but may not be used to provide a quantitative evaluation of bone tissue integration[20-22]. Quantitative ultrasound (QUS) methods represent interesting modalities since they are used clinically in the context of other applications (e.g. teeth, osteoporosis) and are non invasive, non-radiating and cheap. Ultrasound corresponds to mechanical waves and can thus be used to retrieve bone biomechanical properties, but it has never been used to estimate the biomechanical properties of the bone-implant interface in vivo. The feasibility of QUS to assess the bone-implant interface properties has been shown by our group in different papers (see for example [23-25]) and three patents. Finite element models (FEM) have widely been used to simulate the mechanical behaviour of an implant at the organ scale using large sliding contact elements[26]. Classical frictional Coulomb laws have been employed[27, 28], some methods using empirical remodelling laws[29-31], mesh-morphing[32], non linear FEM[33] or statistical techniques[34-36]. However, the existing approaches do not account for the multiscale, evolutive and adhesive nature of bone tissue and most models assume homogeneous bone properties around the implant. The biomechanical modelling of the bone-implant interface often remains simplistic, due to a lack of experimental data at the scale of 100 nm to 100 µm, the main difficulty consisting in that the range of micromovement spans around 100 µm. A better understanding of the biomechanical properties of the bone-implant interface can also be used as input data in FEM to describe and predict implant stability.

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Numerous animal studies[37-39] have studied the in vitro implant stability but most of them are of limited interest for understanding the basic biomechanical phenomena, because the geometry of the different tests leads to spatially complex, non-uniform, multi-axial stress fields at the interface. Specific implant models with a planar bone-implant interface have thus been designed[40, 41] to obtain reproducible measurements. Such model involves the use of flat coin-shaped implants placed on cortical bone of rabbit tibia. However, the tensile tests undertaken so far correspond to an unstable situation and do not allow retrieval of the effective adhesion energy. In recent studies, we have used this implant model to measure the adhesion energy [42], the ultrasonic interface response[43] and newly formed bone properties [44], showing the feasibility of WP1. Objectives. The aim of WP1 is to investigate the multi-time (from the µs up to the month) variations of the multiscale (from the nm to the organ) biomechanical properties of the bone-implant interface as a function of the implant environment, following an approach typical in engineering sciences. The originality of the approach lies in: i) multiphysical multimodality combining ultrasonic measurements, adhesion experiments, scanning acoustic microscopy and nano-indentation, laser confocal microscopy, micro-computed tomography and histology, ii) the development of advanced multiscale bone model accounting for remodelling phenomena and iii) the fundamental nature of this problem (understanding the time evolution of the properties of an interface) with important implications in terms of public health. Another originality of WP1 is to be strongly related to practical applications since industrial transfer are also considered in the domain of medical device. Particular attention will be paid to characterizing and optimizing the physical-biological microstructure (i.e. at relatively high resolution) at the implant interface in combination with the associated bone properties. The influence of parameters such as nutrient carriers, surface roughness, healing time, initial bone-implant distance and normal stresses on the interface properties will be assessed at different scales during bone healing. Adapted analytical and numerical biomechanical modelling taking into account the coupling between the bi-phasic, multiscale and evolutive natures of bone tissue will be developed to optimize the conception of the experiments and to analyse the results. To do so, adhesive contact models coupled with homogenization approaches (mechanics and acoustics) will be considered. The scientific breakthrough will consist in a better understanding of the biomechanical implant stability, which will lead to: i) a better prediction of implant stability, ii) a better conception of the implants, iii) improved surgical strategies and iv) the development of an ultrasonic device to determine and predict implant stability. On the fundamental level, considering the multiscale time evolution of the properties of an interface has many implications in physics and engineering science (eg: glassy materials, composite materials or nuclear industry).

II)1)b Methodology Figure 1 describes schematically the structure of WP1 divided into 3 tasks.

a) Task #1: Development of dedicated biomechanical models and associated simulation The approach derived in WP1 is typical in engineering sciences, with a strong emphasis on mechanical modelling and numerical simulation. Thus, the models aim at: i) designing the experiments, ii) understanding the experimental results and iii) extracting the parameters of interest by solving the inverse problem, which will be done with the help of WP3. Task 1_1: Modelling the ultrasonic characterization of the bone-implant interface. Models describing the interaction between an ultrasonic wave and rough interfaces will be developed and used to solve the inverse problem (estimation of the interface properties, i.e. newly formed bone properties and BIC) from the analysis of its ultrasonic response. The most relevant ultrasonic parameters for the characterization of the stability of the implant will be determined. The influence of the roughness of the implant surface on the ultrasound propagation will be studied using the phase-screen approximation. The effect of the bi-phasic nature of bone tissue on ultrasonic propagation will be accounted for by using the homogenization model published by the PI [16, 17] allowing the determination of bone properties at the tissue scale. The heterogeneity of bone tissue will be accounted for using the Stroh formalism and finite element models [45]. The generic propagation models developed will also be applied to the acoustical problem consider in WP2 for vocal folds, which is also a multilayer structure.

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Once the forward model has been solved, the inverse problem will be tackled. Adapted cost functions quantifying the gap between the experimental and simulated responses will be designed and tested first on simple configurations. Minimization methods developed in WP3 will then allow us to perform the inversion, finding a compromise between high precision and acceptable computation times. Moreover, the smart grid approach developed in WP3 will be applied to the problem of interest in order to determine if positioning different ultrasonic transducers can allow to retrieve more accurate information on the bone-implant interface properties. This point constitutes a strong originality of the project.

Figure 1: Schematic representation of the articulation of WP1 with the three tasks surrounded with dashed lines.

Task 1_2: Multiscale homogenization of newly formed bone tissue. The aim is to provide a reliable multiscale “movie” of evolution of the system. The first step is to continue[16, 17] to develop homogenization theories to ‘take a picture at a time’. Either micromechanics theories (Eshelby’s problem, published by the PI [16, 17]) or asymptotic homogenization (periodic media), will be used to model the mechanical behaviour of osteoid (viscoelastic), woven and cortical bone (both poroelastic). The asymptotic homogenization will be developed based on FEM and FFT models [46] Then, we will ‘take a local movie’ by describing a material point as a mixture of four phases (water, osteoid, woven and cortical bone) using various mixture models inspired by classical models (Voigt, Reuss, self-consistent…). Lastly, we will ‘take a movie of the system’ by accounting for the heterogeneity of mechanical properties by a stochastic FEM and temporal evolution of the regenerating bone tissue. A stochastic distribution of water and osteoid phases will be generated and random walk theory will be used to simulate initial hydraulic and chemical pathways. Due to its high computational cost, this task will make use of the HPC facilities of CAFCI. The development of stochastic simulation approaches will be made in close collaboration with WP3, which develops generic approaches in this field (see subsection II)3).

Task 1_3: Multiscale modelling of the micromechanical contact problem. The aim is to derive a multiscale constitutive relation for the bone-implant interface, taking into account contact behaviour of the rough interface in order to help in the interpretation of the adhesion experiments (modes I and III). The rough interface will be modelled using statistical and fractal models. Both finite element (weak form formulation and adhesive interface layer models) as well as semi-analytical models will be employed at the scale of the representative surface element. Several types of adhesion potential (e.g. van der Walls, elliptic, exponential) will model chemical bonding at the interface, which is important for human well being, but also to understand better how nature bonds. The results of the interface behaviour will be employed in macroscopic contact simulation which will exploit the results obtained in T3_2. Task 1_4: Cross-checking of models and experiments constitutes a critical point of WP1 will be achieved through a comparison of experimental (in vitro and in vivo) and numerical results (in silico), which will help identify possible errors. A benchmarking of the prognostic capabilities of the numerical tools will be realized. To do so, an important task of coordination will be to realize a fusion of the experimental data and to install good communication between modelling and experimental activities.

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b) Task #2: Experimental surgery and experimental analysis of the bone-implant interface

Although mechanical modelling and numerical simulation constitute the heart of WP1, it will be necessary to realize experiments in order to validate the models, which will be done outside of the Polo Científico, at the INTEMA-UNMdP and in the Italian hospital in Buenos Aires. The aim of task #2 is to realize surgical inclusions of coin-shaped implants in vivo under different conditions and to extract multiscale quantitative information on the bone-implant interface, as shown in Figure 3.

Figure 2: (a) Image of the animal model obtained during surgical intervention. (b) Schematic representation of the coin-shaped model.

The surgical procedure has already been validated [23, 42-44]. Superficially modified titanium and zirconium alloys will be prepared in order to improve the osseointegration. Two kinds of procedures are followed. In one of them coin-shaped implants are maintained with elastic strips on the surface of cortical bone. The implants are placed at around 200 µm from the levelled cortical bone surface, leading to an initially empty cavity (bone chamber), which allows to distinguish between mature and newly formed bone tissues. The bone chamber allows: i) standardized mechanical environmental conditions and ii) proper initiation of crack path selection. In the other, cylinders are put by press fit technique in the medullar channel where all the events leading to the formation of the new bone can be study. The different experiments described below will be realized and the results will be compared since the interest of the present approach lies in its multimodal nature. Task T2_1: Ultrasonic experiments. The aim is to investigate the potentiality of QUS techniques to characterize the properties of newly formed bone tissue. The ultrasonic response of the sample will be measured both in transmission and in reflection, for different angles of incidence and frequencies. Feasibility studies[23-25, 43] have shown that the presence of bone around the implant may be assessed, as shown in Figure 3.

Figure 3: Schematic description of the experimental ultrasound measurement device.

Task T2_2: Experimental approaches of adhesive contact micromechanics. This task aims at measuring the effective adhesion energy of the bone-implant interface. Torsion experiments (mode III, feasibility study done [42]) and mechanical cleavage experiments (mode I) will be realized and the results will be compared. Task T2_3: Nanoindentation. The aim is to determine the biomechanical properties of newly formed bone tissue. Nanoindentation techniques (feasibility done [44]) will be coupled with SAM. The competence of a whole bone in the light of its structural units resulting from the remodelling will be analysed. The resolution will be progressively reduced from ~10 µm to ~100 nm. Nanoindentation tests will be done at INTEMA-UNMdP. Task T2_4: Histological analysis. The aims of this task are i) to assess the biological nature of newly formed bone tissue and ii) to assess the bone-to-metal contact (BIC) fraction. Histology will also allow us to assess i) the action of osteoblasts and ii) the mineralization. The feasibility has been shown[23, 43, 44]. Task 2_5: Dielectric experiments. Low frequency (< 5MHz) dielectric properties of trabecular bone strongly correlates with bone mineral content, collagen content and water content, among other parameters [47]. In the microwave range a negative correlation has been found between bone volume / total volume and the permittivity [48]. These techniques will be tested and compared to the simulation models.

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c) Task #3: Technological transfer As mentioned above, WP1 has a strong potential for industrial transfer in the direction of clinical applications. The fundamental works described above allow the development and design of medical devices which aim at providing help and decision assistance to dental and orthopedic surgeons (in the operative room) as well as to rheumatologists and radiologists (imaging modalities of the bone implant interface). Biomechanical approaches will be considered as well as the development of quantitative ultrasound techniques. Such translational approaches have already been carried out since three products are under development, a start-up (WaveImplant) being currently incubated. All three devices are protected via 3 patent applications pending. The first device consists in an ultrasound device aiming at estimating dental implant stability and is shown in Figure 4. The project includes the development and clinical validation of this device, as it was described in the funded ANR project. The second device is an instrumented hammer which can be used during total hip replacement surgery to optimize the implant insertion in bone tissue. The third one aims at estimating bone quality of vertebrae during the surgery. More generally, all components of the ANR-DGOS project OsseoWave are included in the present project and collaborations with the service de chirurgie plastique of the Henri Mondor hospital will be continued. Other material devices investigating the quality of the bone-implant interface will also be developed. Theranostic approaches will also be developed including medical imaging as well as therapy since ultrasound waves have recently been shown to favor and stimulate osseointegration.

Figure 4: Description of the experimental device developed in WP1

Market and industrialization studies will be necessary prior to product development in order to validate the clinical interest of the approach on a large scale basis and its financial viability. Collaboration with medical doctors and surgeons will be mandatory, which will be done in the framework of the Italian hospital (CINEOT/CONICET). The aim will be to bridge the "Valley of Death", where academic ideas are lost from evolving into real commercial solutions for improved Healthcare. This gap will be bridged by focusing on the clinical outcomes and specific clinical constraints that develop into healthcare opportunities. Moreover, a “vulgarization” document which will be edited and will inform the patients on implant surgery. The translational approach developed herein will benefit to the fundamental work developed in WP2 (especially on vocal folds) in order to help define potential application in the field of Otorhinolaryngology.

II)1)c Team members, staff and research environment Table 1 shows the different personals included in WP1, together with their affiliation and competences. The project being highly interdisciplinary, different skills should be represented in WP1 (Modeling and simulation, experimental mechanics, ultrasound characterization, orthopaedic surgeons, material science). The team also includes the collaboration with a French laboratory and with a company (SIDDHI, see

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http://www.siddhisa.com.ar/index.php). Funding will be secured for the first two years of the project since Guillaume Haiat is the coleader of an accepted ANR project starting in 2014. This project includes includes both experienced and young researchers as well as a PhD student, which ensure that the project will have a immediate positive impact on the formation of human resources. The four CONICET researchers from Mar del Plata have taken the commitment to spend one week per month indoor. Romain Bosc will make a long-term mission in Argentina (4 to 6 months).

Name Tasks Institution Competences Status

Guillaume Haiat 1, 2, 3 CNRS

Modeling and simulation [16, 45, 49-54], experimental techniques on bone [23, 42, 44], medical device development [55-57], ultrasound characterization [43, 58], osseointegration [59, 60]

CR1 CNRS, Project leader, WP1 leader.

Adrian Cisilino 1, 2 UNMP /CONICET

Nanoindentation [61], modeling bone remodeling, ultrasound modeling.

Independent Researcher, WP co-leader.

Lucas Colabella 1, 2 UNMP /CONICET

Nanoindentation, modeling bone remodeling, ultrasound characterization PhD student

Josefina Ballarre 2 UNMP /CONICET

Material science for implant conception [61-63] Adjoint researcher,

Cecilia Galarza 1 CSC-CONICET Inverse problem, smart grid [61-63] CONICET Researcher,

indoor María Inés Troparevsky 1 FUIBA/CONI

CET Optimization, Modelling Associate Professor

Silvia Ceré 2 UNMP /CONICET

Material science for implant conception [61, 62], osseointegration [63, 64] Independent researcher,

María Rosa Katunar 2 UNMP

/CONICET Bioengineering and biology Assistant researcher,

Ramiro Irastorza 2 IFLySiB /CONICET Ultrasound Post Doc

Lucas Ritacco 2, 3 CINEOT/ CONICET Orthopedic surgeon [65-67]. CONICET Researcher

Federico Milano 2, 3 CINEOT /CONICET System engineering [65, 66]. PhD student

Salah Naili 1, 2 MSME, CNRS

Mechanical and acoustical modelling [45, 68], histology. Professor UPEC

Vu-Hieu Nguyen 1, 2 MSME, CNRS

Mechanical and acoustical modelling [45, 68], histology. MdC UPEC

Romain Bosc 1 APHP Maxillofacial surgeon. PhD student, UPEC Table 1: Team members and expertises with the outstanding publications

II)1)d Risk assessment Risk Task

1 Task 2

Task 3

Comments

General L M M Substantial preliminary work is completed for T1 minimising the risks. For WP2 and WP3, some preliminary work has been completed to prove the concept.

Attract excellent members

L M M Help from the CNRS and CONICET human resources department. The excellent reputation and networking of the project members will ensure top level recruitment.

Participation of project partners

L L L Each partner has agreed to participate in the project, provide samples and attend workshops throughout the project period.

Mechanical prop of newly formed bone vs environment

M / H

One difficulty lies in the multiscale and evolutive nature of tissues. Much effort will be devoted to the homogenization model (T1), which is a critical point of WP1. A detailed schedule and work plan have been established.

Use of ultrasound to monitor osseointegration

H Despite 3 patents and 5 articles, little work is completed. However, this is a very novel and topical study and the risks are balanced by high potential rewards.

Table 2: Risks associated to WP1

II)1)e Conclusion The biomechanical properties of newly formed bone around implants have so far been insufficiently investigated. An integrative approach is proposed, which emphasizes the development of dedicated modelling accounting for the multiscale and evolving bone properties. Results obtained in WP1 will be used

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in dental and orthopaedic surgery to predict and reduce implant failures. WP1 is pluridisciplinary since it involves biomechanical engineering, physical acoustics, experimental surgery and life sciences, coupling original experimental and modelling approaches. WP1 focuses on engineering sciences because one of the main scientific barriers in the domain of bone remodelling today is related to biomechanical issues, in particular at the microscopic scale. The feasibility of WP1 has been demonstrated and the risks associated are therefore modest compared to the potential benefits. The team has the ability to carry out this study, thanks to the pluridisciplinary expertise in engineering sciences (experimental and theoretical biomechanics, ultrasound and contact mechanics) and in surgery.

II)2 WP2: Fluid-structure interaction and nature French side leader: Denisse Sciamarella Argentinian side leader: Guillermo Artana

II)2)a State-of-the-art and objectives In nature there are numerous systems involving a fluid and a solid interacting to achieve a certain function, of which vibration, propulsion or sound production are examples. Reproducing these systems in the laboratory (in vivo / ex vivo / in vitro) or with the computer (in silico) serves the purpose of observing and studying the mechanisms in play and of capturing the operating principle in simple models. Natural processes involving fluids are complex systems, typically characterized by a rich dynamics. This richness includes moving boundaries, feedback effects, self-sustained cycles, wave propagation, generation of instabilities, large amplitude deformation, nonlinearities in pressure jumps or flow rate relations, flow induced oscillations or collapses, etc. Most of these processes are set forth by a strong fluid structure interaction that is related to the biological function of the system, or with some particular dysfunction. The principles underlying fluid dynamical behaviour in such complex configurations often remain unclear. Investigating such principles leads, not only to the understanding of the natural process itself, but also to the possibility of developing novel technologies on the basis of the abstracted principle. The transfer of function from the natural world to artificial devices benefits from the millions of year design effort performed by natural selection in living systems. Let us mention some examples of these processes. Most channels transporting fluids in living systems are flexible, leading to flow-induced wall deformation. In the case of the phonatory system, for instance, the pressurized air controlled by the lungs interacts with the deformable membranes, known as vocal folds, to produce sound. The air in the phonatory system is often modelled as a fluid line, while recent experiments show that the flow is highly three-dimensional. Apart from involving a strong coupling between the fluid dynamics and the elasticity of the structure, some of these flows are multiphase. Taking up again the example of voice, the vocal folds are a multilayer structure with an outermost layer (mucosa) that encapsulates a fluidlike tissue. In the case of airflow in the respiratory airways (breathing), microfluidics is needed to experimentally model flows in bifurcating channels with control over the geometry. Another example of a natural process depending on the interaction between the flow and a solid with elastic properties can be found in animal locomotion. It is the objective of this WP to model fluid dynamics in natural processes towards the conception of devices with prosthetic or technological aims in both, biomedical and industrial engineering. Modern techniques in experimental and numerical research are necessary to achieve this aim. As for WP1, the engineering approach to this subject is multidisciplinary. In WP2, the involved mechanisms present, in addition to the biomechanical aspects, the complexity associated with unsteady and multiphasic fluid dynamics. A strong cooperation has been taking place between Argentinian and French teams on natural processes involving fluids. These works include the design of in vivo, in vitro and in silico experiments devoted to the swimming of leeches, glottal-like jets, larynx-inspired geometries and self-propulsion of swimmers.

II)2)b Methodology Three research steps are customary when dealing with natural processes: observation, measurement and modelling of the process in vivo (or ex vivo if it applies), artificial reproduction of the process (in vitro and/or in silico), and application of the abstracted principle to engineering. The state of the art in computational fluid dynamics allows for high performance calculations leading to significant progress in the case of the complex flow problems. In silico studies are not only an invaluable research tool: they can also be ultimately

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used with patient-specific prognostic purposes, as in the case of simulations of systems/processes based on CT scans (computerized tomography). For in vivo or in vitro essays involving fluids, specific measurement techniques are required. Velocimetry techniques are often combined with high speed imaging or sound recording. An assessment of the full flow field created, for instance, during vocal fold oscillation, demands three-dimensional flow measurement techniques such as stereoscopic particle image velocimetry. Schlieren visualization is another useful complementary technique. An important issue for in vitro studies is the design of appropriate artificial models capturing the essential features of the natural process that is implemented in the laboratory. This step can be particularly productive since it may lead to innovation in biomedical or industrial processes based on the principle that is being investigated. The specific subjects that the project encompasses can be organized in three main areas, which will naturally present overlaps, since some flow phenomena can be common to different fields.

a) Task #1: Phonation Phonation is the result of the airflow-induced vibration of a pair of multilayered structures called vocal folds. The vibrations of the vocal fold system are mostly passive, i.e. there is no direct active control of vocal fold motion by the brain. Proofs of such passive vibration are phonation in speakers with paralyzed vocal folds as well as the voice-like sound produced with excised larynges in ex vivo experiments. As in other domains, such as anguilliform swimming, the most relevant analytic models are based on very simple fluid flow physics (potential flow approximation). Relaxing the simplifying hypothesis used in simple phonation models leads to a fluid-structure-acoustics problem with numerous theoretical challenges. Not even the origin of sound production is completely elucidated, due to the complex processes in play at the glottis during phonation. The strategy in this task is to face the challenges involved in the complex voice production problem with schematic experimental/numerical models where the specific question under consideration is addressed without contamination of an otherwise excessive number of factors. Complexity in the models can be added progressively with a gradual step-by-step approach. The different elements of the physics of a voice production system are considered in this task using an ensemble of novel experimental and numerical techniques. Experimental work will be done in the Laboratorio de Fluidodinámica de la Facultad de Ingeniería de la Universidad de Buenos Aires (LFD-FIUBA), which has an active cooperation in this area with LIMSI-CNRS. This cooperation has led to a world premier: the 4D reconstruction (3D space + time) of the flow exiting an artificial self-oscillating glottis made of water-filled latex lobes, shown in Figure 5 [69, 70]. The cooperation LFD/LIMSI also led to the development of a rotary flow modulation system mimicking a typical glottal waveform (Figure 6). Infrastructure and facilities in LFD-FIUBA include stereoscopic particle image velocimetry (Stereo-PIV), Schlieren flow visualization, wind tunnels, plasma actuators, 3D printing, and model design. The numerical expertise of LIMSI-CNRS on incompressible [71] and compressible DNS codes will be at the service of the project, benefitting from the informatics infrastructure (clusters, data storage) that will be available at the Polo Científico. Among the variety of questions that remain unattended, our objective is to incorporate two important factors: three-dimensional and multiphasic phenomena. Numerical methods and experimental devices developed to deal with these challenges will admit an almost direct translation to clinical applications, and a less direct but equally interesting translation to technological purposes in bio-inspired flow control. The specific objectives are detailed below.

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Figure 5 (a) In vitro vocal fold model used for a 4D reconstruction of airflow in a glottal cycle with sound-synchronised phase locked measurements

(b) Axis-switching in postglottal flow from stereoscopic particle image velocimetry measurements with self-vibrating vocal fold model.

Figure 6 (a) Prototype of a rotary generator of a

glottal flow waveform

(b)

Glottal flow waveform generated with the rotary valve

Task1_1 Aerodynamics of glottal-like jets From an aerodynamics perspective, the flow problem in phonation has seldom been considered as a jet-case study. The fundamental study of jets dates from the beginning of the 20th century, leading to numerous industrial applications such as control devices using pulsating jets. In nature there is a great diversity of jet types. The glottal jet is turbulent, laterally confined and noncircular. Even if noncircular jets have particular properties exploited in several problems of technological relevance [72], there are only a few volumetric studies of this type of jets. Recent studies of the glottal jet display a spatial structure in three dimensions with causes and functions that need to be fully elucidated (Figure 7). Identifying the fluid flow mechanisms related to the elongated character of the glottal orifice is crucial in order to gain insight into the full field flow problem. Results in this field should lead to a revision of the spatial nature of glottal jet flow [73]. The particular proximity of the subject with jet applications makes the principles investigated in this area suitable for the development of devices with technological/industrial applications. Task 1_2 Fluid-structure-acoustics interaction From a multi-physics perspective, there are several aspects that remain unconsidered in the fluid-structure-acoustics coupling problem at the heart of voice production. This is mainly due to methodological issues. The most advanced numerical approaches propose three-dimensional models relying on coupled continuum and fluid mechanics [74] with an artificially low Reynolds number for the fluid and a structure limited to muscular tissue for the solid (the outermost non-muscular layer of the vocal folds is disregarded). Flow through the glottis is modeled as incompressible. However sophisticated these models may seem in comparison with simplified low-order models, difficulties persist from several angles. Task 1_2 is devoted to two of these problems. When the vocal folds meet during vibration, they collide, deform and close the glottis completely during a part of the cycle that amounts to 25% of the oscillation period in normal voice. In incompressible DNS the full closure problem is generally contoured, using a never effectively closing glottis. A first challenge is therefore to develop a numerical model coping with a normal glottal closure behavior. A second challenge concerns fluid-acoustics

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interaction, which may be satisfactorily addressed implementing a hybrid immersed boundary code with an acoustic solver. The basis for this task will be provided by a code developed at LIMSI to account for coupling between a fluid and a solid deformable structure [75].

Figure 7 (a) Top view of a static vocal fold model

with two semi-cylindrical facing lips (b) Bifurcation and merging of the jet core near the

exit of the static vocal fold model. Task 1_3 Multiphasic phonation As mentioned in task1_2, the non-muscular layer of the vocal folds is not taken into consideration neither in numerical nor experimental approaches, despite its crucial role in determining flow separation, which affects the amplitude and the phase of the aerodynamic forces driving vocal fold motion. This non-muscular layer has been compared in classical textbooks of phonation with a balloon filled with water [76]. Incorporating this layer introduces a liquid phase in the glottal problem which has never been addressed as multiphasic. The modality of the task is mainly numerical. The objective is to develop a code that is capable of including the fluid-like layer covering the vocal folds with a multiphasic approach on the basis of LIMSI codes for diphasic flows with dilatable phases [77]. Task 1_4 In-vivo / In-vitro experiments. This task is experimental and consists in using in-vivo/in-vitro strategies to study particular questions of the different phenomena related to the voice production systems. This does not necessarily imply reproducing the larynx in all its functions. The possibility of isolating some of these functions in laboratory experiments to study them makes part of our biomimetic approach, which is directed to understanding the different principles in action. For instance, in order to consider the bifurcation and merging of the jet core near a static vocal nozzle, plasma actuators will be used to create a localised excitation in the flow. This slight perturbation is expected to enable the spatial interpretation of the instabilities leading to three-dimensional effects through phase locked averaging. Such experiments have a potential that is threefold: they can lead to industrial application in bio-inspired flow control with self-regulating strategies as well as to prosthetic applications in the medical field. Task 1_5 Bio-inspired flow modulation devices. The development of prototypes for controlled flow modulation is the natural continuation of the collaboration between LIMSI-CNRS and LFD-FIUBA that led to the rotatory valve shown in Figure 8. This kind of system is expected to have a clinical (therapeutic) but also a technological relevance in the innovation of novel flow pulsation systems.

b) Task #2: Propulsion The propulsive dynamics of flapping flyers or swimmers relies on the use of an appropriate cycle of storing and releasing elastic energy. The underlying mechanisms explaining how the elastic nature of the wings is related to propulsive efficiency are not yet completely known. The PMMH-ESPCI team has been working for a few years in the physics of bio-inspired locomotion at intermediate Reynolds numbers such as flapping flight and undulatory swimming. The current lines of research concern in particular the strong fluid-structure interactions that arise in these problems. Among other projects, we have focused on simplified models of flapping foils in hydrodynamic tunnel experiments, especially in the dynamics of vorticity in the wake of an oscillating foil ; mechanical models of flapping flyers with flexible wings in a self-propelled configuration (in the spirit of the pioneer experiments of Etienne-Jules Marey), as well as novel experimental models of anguilliform swimming such as the self-propelled elastic swimmers at a free surface that we briefly recall here.

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Figure 8 Schematic diagram of the experimental setup for the elastic swimmer on a free surface. A swimming pool that is placed between a Helmholtz coil pair. The artificial swimmer is constructed with an

elastic cylindrical body with a small magnet embedded at one extremity. The swimmer stays on the air-water interface owing to capillary forces. An AC signal through the coils produces a spatially uniform time-varying magnetic field that drives the oscillation of the magnetic head of the swimmer. The body of the swimmer can

be covered with fluorescent dye that colors the water in the wake of the swimmer (see video in http://vimeo.com/77356211).

Swimmers in nature use body undulations to generate propulsive and maneuvering forces. The anguilliform kinematics is driven by muscular actions all along the body, involving a complex temporal and spatial coordination of all the local actuations. Such swimming kinematics can be reproduced artificially, in a simpler way, by using the elasticity of the body passively. The dynamics of self-propelled elastic swimmers is studied at a free surface in the inertial regime (Figure 6), opening a series of questions that we propose to address in the present project:

1) The problem of quantifying the different sources of hydrodynamic drag in undulatory swimming (skin friction, form drag, wave drag), which has been shown to be non-trivial)

2) The transition between inertial and viscous regimes of swimming (which is piloted by the Reynolds number), a crucial question for certain organisms which change of regime during their development (see Figure 9)

These questions will be adressed together with those arising from the study of the swimming leeches at LFD-FIUBA. The mechanic-sensorial receptors in leeches interest biologists because they are simultaneously simple to manipulate (lifetime, care) and interesting to study. The fluid dynamical study of leech swimming was launched at the request of a group in FCEN-UBA that had a specific question concerning the response of leeches to shear stresses. Leeches are biomimetically interesting because they can be reasonably treated with two-dimensional analyses and computational techniques. The problem concerns many of the open questions in swimming hydrodynamics among which we can mention: the structure of propulsive surfaces, vortex wakes, or surface motion; the effects of the propulsor deformation during locomotion; the effects of unsteady conditions on locomotor kinematics and dynamics; the extent to which the propulsor deformation is actively controlled; the response of the body of the animal to external perturbations; the interaction of propulsive surfaces with natural motions. Velocity field measurements were performed for a Hirudo Medicinalis leech at LFD. These in-vivo experiments were complemented with in-silico studies. Numerical simulations were performed with the numerical code Incompact3d, based on sixth order compact finite difference schemes and a Cartesian grid to solve the incompressible Navier–Stokes equations. An original characteristic of Incompact3d is that this equation is directly solved in the framework of the modified spectral formalism. More precisely, the Poisson solver is only based on Fast Fourier Transforms (FFT) despite the use of inflow/outflow boundary conditions. The time advancement is performed using a second order Adams–Bashforth scheme. The Cartesian coordinate system is set up locally to the body and the movement of the solid is prescribed

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Notice that self-propulsion mechanisms discussed here and self-oscillation that is the landmark of the phonation problem of Task 1 can be seen as two sides of the same coin. In one case, fluid-structure interaction produces the displacement of the solid, while in the other case, the interaction produces the fluid displacement generating sound. Tools implemented to throw light on one of the problems are likely to be profitable for the complementary case. The present project will implement numerical experiments, which are the natural complement of the laboratory setups and can broaden the parameter space used to study the biological mechanisms in play.

Figure 9. Reynolds numbers of zebrafish at deifferent stages of their life [from McHenry and Lauder, 200577]

The specific objectives are: Task 2_1 Numerical simulations with non commercial codes: the problems will be addressed with different immerse boundary techniques, adapting the computing scheme to the research aim. Hybrid forms of compressible/incompressible codes will be developed to deal, for instance, with the problems involving sound production. Validation will be possible through the outdoor laboratory experiments. Task 2_2 In-vivo / In-vitro experiments in the associated laboratories. Experiments are to be undertaken in satellite laboratories equipped with Schlieren strioscopy and Stereoscopic Particle Image Velocimetry. Task 2_3 Development of laboratory biomimetic models, including prototypes of devices with technological relevance. The rotatory voice-producing element is an example. Task 2_4 Drag in undulatory swimmers. During cruising, the thrust produced by a self-propelled swimmer is balanced by a global drag force. For a given object shape, this drag can involve skin friction or form drag, both being well-documented mechanisms. However, for swimmers whose shape is changing in time, the question of drag is not yet clearly established. We will address this problem by investigating experimentally the swimming dynamics of undulating thin flexible foils. Measurements of the propulsive performance together with full recording of the elastic wave kinematics will be used to discuss the general problem of drag in undulatory swimming. Task 2_5 Transition between inertial and viscous regimes of swimming. As mentioned above, this is a crucial question for certain organisms that change of dynamical regime during their lifetime. In the context of bio-inspired robotics, small artificial swimmers may also be forced to swim in inertial as well as viscous regimes. We will use the free-surface swimmer to study this transition, by increasing the viscosity of the fluid using a mixture of water and glycerol instead of pure water. Task 2_6 Interaction between swimmers. A new setup will be designed to study interaction between multiple swimmers. A technological step, or rather a change in the experimental concept, has to be made to avoid perturbing the hydrodynamic interactions between swimmers by the magnetic interactions that would arise if the same setup with coils and magnetic heads of Figure 9 is used.

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Task 2_7 Magnetic swimmers. A resurgence of interest in pattern formation in micro and nano scales has been driven by the prospect of a deeper understanding of the nature of dynamic self-assembly phenomena. Preliminary results showed a pattern of 'snake' composed of localized quasi-one-dimensional segments composed of multiple particles chains structures. These structures were self-assembled from magnetic microparticles suspended in the liquid surface and energized by a vertically oriented alternating magnetic field. Understanding the dynamics of this physical scenario motivates us to propose a theoretical, numerical and experimental study of hydrodynamic flows induced in the vicinity of the swimmer structure pattern. Specifically, we initially seek to determine to what extent it is possible to control the large-scale vortices by varying the frequency of the external magnetic field. From the theoretical point of view, it is proposed to consider a model that coupled amplitude equation for surface waves with the equation for the large scales of the flow, in order to describe the experimental observations. This joint study would provide valuable information also on the mechanism of the formation of dynamic patterns.

c) Task #3: Wind The main goal of this task is to establish a methodology for simulating interaction phenomena between wind turbines and their surroundings. This is a problem where, typically, very different spatial and temporal scales appear. On the one hand, this methodology will allow us to study the effects that meteorological conditions have on wind turbines through aerodynamic loading. These results are particularly important for the next generation of turbines with rotors exceeding 150 m in diameter. On the other hand, it is foreseen to improve the forecast of wind power production through numerical weather prediction models. Wind energy differs from other conventional energies sources due to the stochastic nature of wind. This makes its forecast a key factor for increasing the amount of energy produced from this source. In previous works we developed a coupled model [78, 79] suitable for dynamical simulations of the behavior of wind turbine blades. This model requires low computational cost since the blades are modeled as one dimensional solids and the fluid dynamical model, which is algebraic, is based on the Blade Element Momentum theory. This makes it particularly suitable for coupling with other models that simulate the turbine with a low computational cost. The specific goals for the project include: Task 3_1 Aeroelastic phenomena around blades and wind turbine - Evaluation of different wind turbine blades design criteria. - Improvement of the aerodynamic behavior of blades. - Addition of modules representing different turbine components. - Wind tunnel experimental analysis of wind turbines in "turbulent wake state" [80, 81] Task 3_2 Simulation of the interaction between wind turbines and their surroundings. - Development of a methodology for numerical simulation of the interaction between large wind turbines and the atmospheric circulation in lower layers. - Coupling of the numerical weather prediction model WRF with a reduced algebraic wind turbine model. Task 3_3 High performance computing - Implementation of the above mentioned models in a high performance computing environment.

d) Task #4: Jets The study of jets is a field that has applications common to the automotive industry (engine intake, EGR, exhaust pipe) and to the biomedical field (blood flow, respiration, phonation). This coincidence is particularly appropriate to propose studies that contribute to research progress in both directions. Objectives in task #1 already illustrate this connection, without exhausting all the possibilities proper to jets. The particular goals of this task include the development of unsteady flow-meters, filters effects in arteries and starting immersed jets. The dynamics of starting immersed jets has been widely studied in turbulent regimes due to their important role in different industrial applications such as fuel and oxidizer jets in combustion chambers, inkjet printers, or flow control, especially control of separated flows [82]. Laminar starting jets, however, have received less attention. Flows of this kind can be characterized by the evolution of a vortex head and jet stem formed between the nozzle and the structure (see Figure 10).

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Figure 10. Lateral view of a starting micro jet with a wall in proximity at the first stages of the flow

In the analysis of laminar starting jets, it is of interest to undertake studies with stable jets. Being the jets highly unstable, one of the best choices that appeared recently is to consider jets issuing from orifices of very small sizes (microjets). In such a case the jet stem remains less prone to develop instabilities (sinuous or varicose) and the flow remains less complex. One possible explanation of this enhancement of stability can be associated to a low level of the background perturbations with short wavelengths (or high frequencies) that these jets would require to develop instabilities with a high growth rate. Besides the academic interest, applications of such configurations can be found in fluidics or microfluidics devices. Instantaneous switching of submerged laminar jets can be used to perform analog or digital operations similar to those performed with electronics. These jets enabled the development of devices like fluid amplifiers which are of interest in environments where electronic digital logic would be unreliable, as for instance in systems exposed to high levels of electromagnetic interference or ionizing radiation. In the domain of biology, these types of jets are used for instance as a stimulus to study the response of type T mechanic-sensorial receptors of the Hirudo Medicinalis leeches to shear stresses [83]. Laminar starting jets are also studied in relation to biological propulsion mechanisms [84]. The presence of a wall at close proximity of the starting jet may affect the vortex structure of the head and the instabilities developed in the trailing jet. It is the effect of this confinement that will receive particular consideration in the present line of research. Task 4_1 Unsteady flowmeters The aim is to measure the unsteady flow through the EGR loop of a prototype engine. Implement unsteady flowmeters in such pipes remains a challenge due to measurement conditions (temperature, soot) and high level acoustic waves. Task 4_2 Pulsating flow in arteries. The particular question that will be addressed in this subtask concerns filters effect: stent and vena cava filter on the pulsating flow in the arteries. Task 4_3 Starting immersed jets The goal is to undertake numerical and experimental studies of starting jets of reduced size and discriminate how they are influenced by the presence of walls in their proximity. Cases considered will be axisymmetric jets and slot jets. Task 4_4 Microjet Actuators for flow control We aim with this task at characterizing flow actuators consisting on single or multiple microjets to modify separated flows. Jets produced either tangentially or normally to the wall will be taken into account. We will explore the possibilities of producing pulsating microjets either with self oscillating devices or through rotating valves. Goals to be achieved are to obtain high frequencies (larger than kHz) systems and analyze the authority of these devices to control flows like the wake of the rotor of wind turbines and the loads associated to the fluid structure interaction.

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II)2)c Team members, staff and research environment Name Tasks Institution Competences Status

Denisse Sciamarella 1,2 CNRS Nonlinear dynamics, Topology of chaos, Voice production systems, Fluid mechanics: DNS, Experiments in fluids

CR1 CNRS, WP leader, Indoor

Guillermo Artana 1,2,3 FIUBA/CONICET Experimental, theoretical and numerical fluid dynamics

CONICET Researcher, WP co-leader, Outdoor

Alejandro Otero 3 FIUBA/CONICET Fluid-structure interaction, Eolic energy, Aeroelasticity, Numerical simulation, Parallel computing

CONICET Researcher, Indoor

Ramiro Godoy Diana 2 PMMH-ESPCI/CNRS

Experimental fluid dynamics for ocean wave energy, geophysical fluid dynamics, wake instabilities and bio-inspired propulsion

CR1 CNRS

Benjamin Thiria 2 PMMH- ESPCI/CNRS

Fluid structure interaction, bio-inspired propulsion, wind power turbines

MC Université Paris Diderot

Pablo Mininni FCEN Fluid turbulence, CFD simulation, Complex Systems

CONICET Researcher

Guillaume Haiat 1 CNRS Modeling of acoustical behavior CR1 CNRS, indoor

Pablo Cobelli 2 FCEN/CONICET Wave turbulence, free-surface flows, non linear interaction in water waves, bio-inspired swimming.

CONICET researcher

Etienne Mémin 1,2 INRIA Fluid flow analysis and model reduction, data assimilation, wavelets, numerical mathematics

INRIA Researcher

Alejandro Gronskis 1,2 FIUBA/CONICET

Development of Direct numerical simulation methods for problems with immersed boundaries, inverse problems

CONICET Researcher

Martín Cabaleiro 2,3 FIUBA/CONICET Microfluidics, jets CONICET Researcher

Pierre Audier 1 Programme Bernardo Houssay Plasma actuators Post-doc Bernardo

Houssay

Florian Tuerke 1 CONICET/CNRS Flow instabilities. DNS. Fluid dynamical coupling between flows in a face-to-face cavity

PhD in co-tutelle FIUBA/Université de Paris-Sud

Fernando Minotti 2 FCEN/CONICET Fluid dynamics. Insect locomotion. CONICET researcher Thomas Duriez 2 FIUBA/CONICET Flow control, fluid dynamics CONICET researcher

Juan D’Adamo 2 FIUBA/CONICET Fluid dynamics, hydrodynamics instabilities, Wind tunnel experiments, PIV.

CONICET researcher

Bérengère Podvin LIMSI-CNRS Synthetic boundary conditions for numerical simulation, POD applied to instabilities

CR1 CNRS

Virginie Daru LIMSI-CNRS Solvers for numerical simulation MCHC ENSAM

Ivan Delbende LIMSI-CNRS Turbulence, numerical simulation, instabilities MC UPMC

Eric Foucault PPRIME-CNRS Pulsating flows in pipes, usteady flow-meters

Professor, Université de Poitiers

Christophe Louste PPRIME-CNRS Microfluidics, electrofluidics in porous media (bones) CNRS Researcher

Ramiro Godoy Diana will spend time in Buenos Aires through long term missions lasting several months.

II)2)d) Conclusion

The natural processes involving fluids proposed as research topics in this WP have a direct relevance in numerous fields ranging from clinical tools to flow control devices. Open topics regarding phonation, propulsion and microfluidics in natural processes have been described, with specific objectives and common tools and approaches. Results in WP2 will include experimental approaches/devices for research, an

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understanding of the operating principles in biological functions concerning vibration, propulsion and transport, and the development of numerical tools adapted to complex problems with strong fluid-structure-acoustics interactions, including large deformation and collapse. The undertaken risks are those proper to a fundamental research work in which potential benefits are always ahead. The team has the expertise that is necessary to develop, implement and use the numerical methods and the experimental facilities. The synergy between the different tasks hosted by WP2, and with WP1 is also promising.

II)3 WP3: Inverse problem French side leader: Etienne Mémin Argentinian side leader: Cecilia Galarza

II)3)a General formulation General inverse problems are encountered when using analytical models for describing problems related with engineering or experimental sciences such as the one described in WP2 and WP3. Consider the following equation:

(1) where A : X→ Y , and X, Y are functional spaces. There are two problems naturally associated to (1): • The forward problem (FP): find for a given • The inverse problem (IP): find when is given Several examples of interest are formulated under this framework. Medical imaging, image processing, mathematical finance, astronomy, geophysics, non-destructive material testing, etc..., are examples of different areas where solving inverse problems provide concrete solutions. We will first describe the general characteristics of an inverse problem for then going to specific examples of application to technological problems. Formulation of an Inverse Problem. In general the IP (1) cannot be solved exactly and numerical methods must be developed in order to find approximate solutions . By doing so, caution should be taken since mall perturbations in the data may produce large errors in the solution, i.e. the set : ||f- || < ε is unbounded. This would be the case, data that differ significantly might solve (IP) and the answer will lack of interest. In addition the function data g is usually perturbed and only noisy data that satisfies g- < ε is available. According to Hadamard, (IP) is well posed if Definition. An IP is well-posed if 1. for each there exists at most one that satisfies Af = g (uniqueness) 2. for there exists at least one solution (existence) 3. f - .X → 0 if g - Y (continuity of the solution with respect to the data g) As we mentioned above, the data g is usually perturbed with noise and it is not always available for all x. Thus, the information to carry on the inversion is not complete and it is usually contaminated with noise. There are different inverse problem methodologies in the context of dynamical systems or mathematical model parameter estimation that take this fact into account when a sufficient number of observations of one or more states (variables) are available. The general objective of the proposed research is to develop theoretical and practical algorithms for solving inverse problems on a wide range of applications present in the MoSiME project. We propose to apply mathematical tools coming from different areas such as functional analysis, optimization, statistics,

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numerical analysis, taking into account particular properties of the problem involved in order to solve the inverse problems in different mathematical frameworks. We will take advantage of the particularities of each problem in order to develop new tools or combine classical ones for solving the associated inverse problems. The research will be performed along four axis with strong interaction among them: solutions to parametric inverse problems; data assimilation for large scale systems; fluid flow dynamic coupling; analysis and design of large scale observation networks. We describe below the specific details of these topics.

II)3)b Methodology We consider four different tasks to be performed in this workpackage

a) Task #1: Solution to parametric inverse problems Parametric mathematical models like the one given in Eq. (1) can be used to describe dynamics arising from biological, physical and engineering systems such as the ones described in WP2 and WP3. When the parameters are known, the system can be used for prediction, control and simulation. But in general the value of the parameters is not accurate and it is estimated from experimental data. Since simulation, prediction and control of the underlying process strongly depend on the parameters, it is important to estimate their values accurately before performing these tasks. Estimation is generally formulated from experimental data and it can be formulated as an inverse problem. If we consider that the output of a dynamical system that models a process is the data, parameter estimation is an inverse problem too, which is related to the transformation from data to model parameters. The IP consist in extracting the parameters of the model from the data while on the other hand, the corresponding FP corresponds to determine the data from the parameters of the model. Parameter estimation problems are usually formulated as optimization ones and they are relatively difficult to solve accurately because of their nonlinear nature. Recently, several authors have developed a design framework to determine when (or where) to collect samples in order to have useful information to carry on the estimation process [85-87]. To be more precise, we consider a stationary process modeled by a parametric equation:

Af(x,θ ) = g(x) (2) where θ Rp is the parameter to be estimated, is the set where the independent variable x evolves (in the case of dynamical systems it is an interval of time), , , and g : Rn →R are smooth. We suppose that there exists a real value θ0 such that the (2) describes the process. The output (data for the inversion process) is

u(x, θ) = C f(x, θ)

where C is the observation operator. Most of the time, only a discrete set of output data is available, we assume that the value of the output corresponding to the true parameter θ0 has been measured at n points Λ = x1, .., xn, obtaining the data:

u(x1, θ0 ) + ε1, … , u(xn ,θ0) + εn ,

where ε1, … , εn , represent the noise. In general, εi are supposed to be independent realization of a centered normal random variable with variance σ2. In this context the (IP) corresponding to the parameter estimation reads as the estimation of the unknown parameters θ0 from the set of noisy data:

ui := u(xi , θ0) + εi , i = 1, … , n

Parameter estimation can be formulated as an optimization problem if we consider the ordinary least square method to estimate θ0. Considering the ordinary least-square (OLS) functional

J (θ)=∑ i= 1

n(u( xi ,θ0)− ui)

2

the OLS-estimate θˆ of θ0 is defined as

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(3)

where A is the set of possible parameter values. Note that θˆ in (3) is a realization of a random variable Θˆ due to the presence of the random noise. To statistically analyze the estimation, we may formulate a statistical model [88] of the form

Y(x) = u(x, θ0) + Ξ(x) , x ∈ Ω

where θ0 is the true value of the unknown parameters and Ξ is a vector random process that represents observation error for the measured variables. Realizations of the statistical model (4) can be written as

y(x) = u(x, θ0) + ε(x), x ∈ Ω

It can be proved that under suitable hypothesis, Θˆ is an asymptotically normal variable (see [88, 89])

Θˆ < N (θ0 , (σ2 F (x1 , … , xn, θ0 ))-1) , (4) where F (x1 , … , xn, θ0 ) ∈ R p×p is the so-called Fisher information matrix.

F ( x1,... , xn ,θ0)=∑ k= 1

n ∂u∂ θi

u( xk ,θ) ∂u∂ θ j

u( xk ,θ)

The partial derivatives ∂ u∂θ j

u( xk ,θ) are the traditional sensitivity functions that, assuming smoothness on u,

quantify the variations in u with respect to changes in the component of the parameter. A precise discussion on the hypothesis and the approximations involved in the above statements is given in [89]. Optimal Design Techniques. Since in practical experiments it is important to avoid running the experiment many times because of its cost or because it involves invasive procedures, it is important to have some criteria to determine where samples should be taken. This is the goal of the optimal design techniques: to look for the optimal set of observation points in order to carry on the estimation process. Different criteria give rise to different selection of observation points. In general, optimal design methods for parameter estimation problems choose the sampling distribution by minimizing a specific cost function related to the error or to the accuracy in parameter estimates. Data collected in this optimal way will lead to parameter estimates with increased accuracy. In view of the asymptotic distribution (4)-(5), it is natural to choose the points xi that minimize F (x1 , … , xn, θ0 ) in some sense (see [85, 86]). There exist different optimal design criteria, among others:

• maximum absolute value of the incremental generalized sensitivity function (IGSF), • D-optimal criterion [85, 86] • SE-optimal design criterion[87].

The D-optimal criterion is a well-known and widely used optimal design method that consists in minimizing det F (x1, ...xn, θ)-1. Geometrically, it corresponds to minimizing the volume of the confidence ellipsoid for the covariance matrix Cov = σ2F-1. With that purpose we choose the set ΛD = x1,D . . . , xn,D of n observation points where the measurements y1, ..., yn are to be taken by minimizing the function

G(x1, ...xn, θ0) = det F (x1, ...xn, θ0)-1 , x1, … , xn ∈ Ω

starting with some initial set of points and considering θ0 as an initial guess value for the parameter. After having selected the observation points, we perform OLS with the optimal set ΛD and the initial guess θ0. These techniques were successfully applied for a six-compartment HIV model and an enzyme kinetics model of the Calvin Cycle in spinach. Numerical results for a distributed parameter system in a 3D one layer spherical domain are presented. These methods will also be applied in the framework of the project in particular for solving the inverse problem described in WP1 given by the determination of the material properties of the bone-implant interface based on its ultrasonic response.

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Activities to be performed (Task 1): • Algorithm development for optimal design in the presence of ill-conditioned partial differential

equations (PDE) models in different multi-layered domains: In order to determine the best instants or locations (sensor placement) at which measurements will be taken for solving inverse problems, we will study optimal design techniques, sensitivity analysis, properties of the Fisher information matrix and preconditioning methods. Problems of this type appear in different contexts such as detection of structural anomalies or damage detection in multi-layered media, epileptic source detection from electroencephalography data, and detection of underground sources in geological structures. These problems present a huge computational challenge since the sensitivity matrices that arise in these cases may lead to ill-conditioned design matrices. For example, Fisher information matrices may have very large conditioning numbers. Therefore, several optimal design techniques cannot be applied directly. We will study the implementation of additional techniques such as multiscale and conditioning operators in order to develop computationally efficient schemes for optimal design

• Development of new tools, based on wavelet transform, for parameter estimation and computation of approximate solutions to inverse problems associated to integral operators: The problem of finding an accurate solution to Inverse Problem (IP) associated to integral operators by means of Wavelet decompositions has been analysed in different works. Wavelet Galerkin Methods have been proposed to find approximate solutions to IP, in particular, Wavelet Vaguelet Decomposition Methods were developed to solve (IP) with Pseudodiferential Operators that admit a wavelet-vaguelet decomposition that can be computed efficiently. This decomposition has the advantage of taking into accountcharacteristics of the operator and also of the data. In this framework and based on these results, we will develop decomposition methods to approximate the solution f when the data is given only at discrete points xj . Solutions to the forward problem (FP) will also be addressed. The wavelet transform and Galerkin type projections into a suitable wavelet space well adapted to the problem will be applied.

• Algorithms design for inversion of the generalized Radon transforms in scattered radiation imaging: We will analyze different reconstruction algorithms of generalized Radon transforms such as methods based on analytic formulae with ad-hoc backprojections and on algebraic inversion techniques. Although conventional emission systems for tomographic imaging operate only with unscattered radiation, there is a new imaging principle that takes advantage of Comptom scattering in order to perform reconstructions. Data acquisition stage is modeled by the three-dimensional Conical Radon Transform (CRT), and its less realistic two-dimensional version, the V-line Radon transform. Since these transformations are analytically invertible, image reconstruction from data is guaranteed. The aim is to implement different reconstruction algorithms of these generalized Radon transforms and to compare their performances. We will focus on methods based on analytic formulae with ad hoc backprojections, and also on algebraic inversion techniques.

b) Task #2: Image data and fluid flow dynamics coupling Data - Model coupling: challenges and difficulties It is worth recalling that the understanding and the forecast of many flows are of crucial interest for our everyday life. This concerns activities or domains that encompass risk or catastrophe management, monitoring of extreme events, biomimetic aspects developed in WP2, transportation logistic issues, economic activities and the study of climate change. With respect to this last element, a detailed understanding of the physical mechanisms that take place in atmospheric or oceanic circulations is inescapable to infer regional climatic changes. Considering these few examples, we can steadily remark that, either for analysis or forecasting purposes, all those domains depend strongly on numerical models defined at various temporal or spatial scales. As numerical flow models covering the entire hydrodynamics scale -ranging from the integral scale (1000 km) up to the dissipation scale (1 mm)- is far beyond the reach of computers, only large-scale modeling can be handled. The effect of the small scales in terms of kinetic energy dissipation or backscattering must be modeled. Forcing terms taking place at scales much smaller than the resolution grid must be introduced. Those strongly intermittent processes, located sometimes in precise areas of the globe and with strong seasonal variations, must be judiciously introduced to provide an accurate modeling of the variables describing the state of atmospheric or oceanic flows at a future instant. This tricky modeling task becomes excruciatingly difficult when one aims at introducing biogeochemical processes or to take into ac- count the different human activities that either potentially impact the environment or depend upon it for sustainable development questions.

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For several years geophysicists can rely on a physical analysis of gigantic mass of data provided by a vast amount of imaging sensors such as Lidar, coastal and weather-watch radars, and satellites orbiting around the globe. All those image data are dense and allow observing range scales smaller than the model resolution grid. Logically, one would like to incorporate in numerical models complex nonlinear phenomena such as front, or tornadoes that are straightforwardly observed in image data sequences and which are, at the same time, difficult to generate spontaneously from models. The coupling of such data in oceanic or atmospheric numerical models is, unfortunately, not as easy as it may appear at first glance. The essential difficulty follows principally from the fact that the observed luminance does not belong to the system state space. In general, there is indeed a complex relationship between the luminance function and the flow state variables that leads to difficult inverse problems to recover for instance velocity, temperature or pressure variables from a set of images. The other source of concern lies in the fact that images and models do not live at the same scales. This ever increasing scale difference, caused by satellite sensors technological progress, induces a scale relation, which further complicates the connection between the state variables and the observations. And, as indicated previously, large scale models are only imperfectly known. Small scale forcing and geophysical turbulence models must be set up. All these difficulties constitute a new challenge for the analysis of geophysical flows. In particular the coupling of satellite image data and models is a very challenging issue. Such issue also extend naturally to experimental and industrial flows visualized through laser sheets and particles. For these flows issues of volume reconstruction and data driven simulations are of the highest importance in several application domains in order to unveil and understand the physical mechanism involved. Methodological frameworks for data-models coupling The coupling between data and models gave rise to an important research effort, and several mathematical methodologies have emerged. It is, indeed, not possible to consider a unique framework for those methodologies, as the nature in itself of the coupling depends on the targeted application. Three different prototypical cases can be envisioned.

The data is inherent in the numerical model definition. The data constitute the basic element to define an empirical representation of the dynamic model. General physical principles are eventually used in addition in order to constrain this definition. Monitoring applications (pollutant sheet drifting, ethological model of marine species, etc.) or short-term forecasting applications (convective system forecasting, extreme event monitoring, etc.) involving complex small scales phenomena are of central interest in this case.

The physical model and the data are tightly coupled to define the numerical model. In this situation data are used to calibrate or to define terms of the dynamic models. Models at regional scales are here central. This case includes, for instance, the definition of subgrid terms from features extracted from image data (velocity fields, isolines, histograms, etc. ), or the estimation of specific model parameters. Data assimilation strategies play a major role for this purpose. The physical model constitutes the main actor. Data is used in this context to validate the model or to constrain some parameters of the dynamics like error terms or the initial condition. This case concerns situations at larger scales such as coupled ocean-atmosphere models. In contrast to the two other cases, here a long-term forecast is sought. Hence models cannot depend too strongly on data.

These three cases define radically different contexts that correspond to the distinct research issues we have started to explore. In the first case, where one seeks to define empirical dynamic models from data, several recent methods must be intensively explored. These include projection techniques on dedicated empirical bases [90], approaches for the tracking of characteristic elements [59], and the estimation of dynamical transfer operators [91][62]. In the second case, methods of interest consist of image based data assimilation techniques [91, 92] or frameworks for the definition of small-scales stress tensors from uncertainties [93]. The third case relies on model reduction strategy built from physical considerations. Data can be used for validation or for parameter calibration purposes. Strategies of particular interest here are the construction of stochastic reduced models expressed as a decomposition of the system between variables that evolve slowly in time and unresolved small scale variables with a rapid temporal decorrelation property [93, 94]. Beyond proper methodological challenges for each of those axes, several theoretical or computational common issues are otherwise essential.

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The new computational possibilities provided by massive parallelism The availability of the large grid computing systems in GIOL allows the establishment of assimilation methods based on an ensemble of samples. These Monte-Carlo methods - referred to as "ensemble methods" or "particle filters" - enable the implementation of stochastic filters or optimal control techniques that do not require explicitly the construction of the tangent linear dynamic operator's adjoint. Besides avoiding a tedious phase in the implementation of optimal control assimilation techniques, these techniques have the advantage of taking into account the full nonlinearities associated with the system either in terms of the dynamics or in terms of the observation transfer operator. They can, as a consequence, be applied on larger temporal window and, in theory, provide access to statistics on plausible realizations of the system. This point offers new interesting possibilities in the physical analysis of the system if one has representative system trajectory samples. A directly related perspective concerns the issues of the results analysis and visualization. It would be, indeed, very interesting to devise extensions of Lagrangian investigation tools on these ensemble of samples [95, 96]. Such exploration techniques could enable one to validate or invalidate the ability of models to reproduce large scale transport mechanisms involved in oceanic thermohaline circulation. The exploitation and the interpretation of high order empirical moments, which are for the moment inaccessible, would constitute extremely interesting information sources for analysis. Model uncertainty and stochastic parametrization State-of-the-art ensemble assimilation methods have a tendency to underestimate the variance of the assimilation error but also to over-estimate covariance values between two far-apart grid points. This deficiency reduces the predictive skill of the ensemble. It is due essentially to the low amount of ensemble members that can be handled compared to the state space dimension, but it is also caused by an insufficient coverage of the errors attached to the dynamic model. These errors are difficult to quantify or to characterize. Furthermore, to be managed properly they require the introduction of a stochastic representation of the dynamics. The Gaussian choices which are usually done represent poorly the uncertainty transported by the considered models. Alternative definitions of stochastic dynamics constitute a broad multidisciplinary research domain that requires a coupled multidisciplinary research effort in data analysis, applied mathematics and geophysics. As a matter of fact, one aims on the one hand to improve the predictive skill of ensemble data assimilation techniques. But, on the other hand, one also aims at modeling by way of random fields, the contribution of unresolved scales or rare events. Indeed the definition of this set of models covers the three kinds of data-model interactions exhibited previously. This need covers the definition of empirical models where a partial representation of the state space from observations (for instance the reconstruction of oceanic streams at the sea surface from 2D temperature map) must be compensated by an uncertainty on the system components that are not directly modeled (the depth currents). This concerns also large scales models such as coupled ocean-atmosphere models, in which interaction bulks and small scales intermittent events must be taken into account. Finer scale models require, as well, the incorporation of uncertainty terms to represent accurately the action of small scale processes of critical importance such as bathymetry or atmospheric boundary layer turbulence. The calibration of the probability distribution involved in those stochastic parameterizations could be done from the data and/or from physical arguments. The use of stochastic approach in modelling and simulation constitute the heart of the project since it will be developed in WP3 and used in WP1 (to model newly formed bone tissue) and WP2 (see above). Activities to be performed (Task 2) We list below some particular issues related to the general research directions evoked above and that we intend to investigate:

• The study of efficient ensemble data assimilation procedures which might allow the accurate estimation of empirical moments of plausible trajectories of the considered system. It concerns the continuation of the works based on conditional diffusion techniques initiated recently • The extension of uncertainty modeling proposed in [93] to the case of rotating flows for geophysical models. We wish for instance to extend this modeling to the rotating Boussinesq equations, the Quasi-Geostrophic models, the linearized primitive equations or to essential theorems such as the Ertel Theorem, which governs potential vorticity conservation. The exploration of coupled atmospheric oceanic models would be also of interest. • The investigation of efficient procedures for reduced order modeling either from data or from physical considerations. Coupled with the previous strategy, the objective will be to devise efficient stochastic reduced models for geophysical flows.

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• Continue the effort on the design of techniques allowing the definition of data driven empirical models derived from tracking methods, as in [29]. We believe that data driven tracking techniques and online learning of dedicated dynamics may bring efficient alternatives for short term forecasting applications involving phenomena that would require excessively complex models (i.e., pollutant sheets drifting for instance)

All these research issues illustrate several instances of the data and model coupling problem. They all rely on common methodological tools and should hence be explored in tandem. Furthermore, we advocate, the central role played by image data in the proposition of efficient methods to derive powerful flow models in the environmental sciences or in industry. The research directions envisaged are in the direct continuity of the strong collaborations established between the Fluminance Inria team, the group of Pablo Minnini and the one of Guillermo Artana. These collaborations have led to numerous publications in first rank conferences or journals and cover applications domains ranging from experimental fluid mechanics to geophysics through computer vision.

c) Task #3: Large communication networks for control and remote sensing Networked sensing systems with the ability to collect, analyze, and transmit data about their environment are driving numerous innovations in the area of information technology. Spatially distributed sensing and actuation provide the means for envisioning systems that will be able not only to adapt to the environment, but also to modify by themselves previously established rules. The effective implementation of these systems is a multidisciplinary effort that involves specialist in control, communications, signal processing, and digital systems designers [97]. Within this context, one recurrent problem is to detect the presence or not of certain event identified by a discrete random vector that may take only l possible values. From an analytical perspective, consider a spatially distributed random process, Y(t, x), where t is the time and x is the vector of spatial coordinates. Moreover, consider that

Y(t, x) = F(t, x, θ) , (6)

where F(., ., .) is an operator between appropriately defined functional spaces, and θ < pΘ. Samples of Y(t, x) are available at a discrete number of locations and at discrete intervals of time. For instance, let y(k, i) = Y(kT, xi) be the data collected at the i-th node (i = 1, … , n) located at xi at time kT . In a networked system, y(k, i) is available only after having been transmitted through a communication channel. Let z(k, i) be the received version of the data. Then we consider that

z(k, i) = αiy(k, i) + ni(k), (7)

where αi is a random variable that represents the multipath fading and ni is the background noise of the communication channel. In the classical scheme, the decision is constructed in a fusion center using the set of data Z = z(k, i), k = 1, · · · m, i = 1, · · · , n. In this case, each node is a measuring unit only with no extra computing capacity. For improving on the detection performance, one needs to guarantee a reliable link between the nodes and the fusion center when limited resources are available and the phys- ical link has severe impairments (path loss, multipath fading, disturbances, etc) [98, 99]. When the process to be detected is spatially correlated, a more efficient solution is to perform partial computations among neighboring nodes before merging all the information together [100]. In this case, define the set Ii = xj : xi and xj are in the same neighborhood, for an appropriate definition of neighborhood. Then, for a judiciously chosen function f (.), the signal transmitted by the i-th node takes the form

z˜(k, i) = αif (y(k, i), z(k, Ii)) + ni(k), (8)

where z(k, Ii) is the set of signals exchanged over the network by the nodes in Ii [101].

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T3.1 Asymptotic performance study A sensible question is how the detection system performs in the ideal case when the numbers of nodes and time intervals grow with no limit. Albeit the answer will not be realizable, it is clear that any real system will only worsen its performance with respect to the asymptotic performance. In particular, for a detection problem, the performance is measured by the error probability. When working in the asymptotic regime, the notion of error exponents is available. Let pn be the error probability associated with n nodes. Then, the error exponent ε is defined as:

ε= lim n→∞

− log pn

n (9)

To perform this analysis, it is convenient to use the theory of large deviation[102] that establishes the rate at which a sequence of probabilities approaches its limit. Since we are working with distributed detection problems, performance depends not only on the detection strategy but also on the way nodes communicate among them. In [103], orthogonal communication channels have been used, avoiding multiuser interference among nodes, but increasing the required bandwidth or the trans- mission delay to transmit the data. To tackle this problem, the use of a multiple access channel (MAC) has been proposed in [104], where all the nodes transmit simultaneously sharing the bandwidth. However, this simple scheme is not suitable when detecting correlated process. In [100] a scheme is presented that works well with low correlation processes but it is restricted to autoregressive processes of order 1 on a line. In this project, we pose a detection scheme where each node has a triple ability: it can sense its environment, perform some processing, and communicate with other members of the network. Each node in the network acquires data from its environment. Using this information and some other information gathered from neighboring nodes, each node communicates a function of its own measurements and those obtained from its neighborhood. Using the natural superposition of a multiple access channel, linear combinations of the local functions are transmitted. These partial computations are then directed to a fusion center where the final decision is constructed. Under this setup, we analyze a binary hypothesis problem under the Neyman- Pearson framework, where each sensor transmits a scaled version of the acquired measurement through a multiple access channel. Using multiple orthogonal channels, different linear combinations of the node measurements are communicated to the fusion center. A trade-off is then established between the amount of orthogonal channels required and the power transmitted by the nodes for each signal-to-noise ratio of the communication channel. An interesting optimization problem may be posed to obtain the communication strategy that gives the largest error exponent. T3.2 Smart Grid In recent years, the idea of upgrading the electricity grid by distributing sensors and control units along the network has spurred new research activity and technology development. This effort has been motivated by several factors: existence of complex and sophisticated electrical systems that cross country borders in some occasions, strategic changes in the energy matrix by including renewable energies, advancements in the design of digital systems and communication networks. Projects at different levels of the grid have been envisioned to build a large sensor network on top of the grid to monitor the grid activity and to optimize its performance. Towards this direction, phasor measurement units (PMUs) are being deployed along the energy transmission and distribution networks. These measuring devices use the time reference from the GPS system to establish phase difference between distant points of the network. In addition with a two-way communication network, this allows a real-time power grid state estimation that could be used to perform early fault detection. Moreover, using PMUs it is possible to retrieve some information from events occurring on zones that are geographically separated but whose combined effect may be detrimental for the general system [105-107]. Fig. 11 shows the topology of the transmission network on the north area of Argentina. It is clear that PMUs will be deployed at distant points in the country.

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Figure 11: Topology of the Argentine Interconnected System (SADI) for HV transmission network. PMUs can be used for cost optimization of generation and distribution of electric energy. Although there are a set of well known techniques for power grid estimation [108], these cannot be performed on real-time and more sophisticated tools are needed to overcome this limitation and therefore be able to make decisions with shorter time of response. The general problem, is posed as a distributed estimation and detection problem as those presented earlier. Moreover, the PMUs will be the nodes of the graph representing the sensor network and the problem will be to obtain a state estimation based on measurements from the graph nodes. Optimal filtering, Kalman filter or more generally particle filtering, will give us the necessary tools to solve the problem [109]. Given the spatial correlation of the electric power signal, states corresponding to different positions in the network can be observed through a single PMU measurement. However this could be not the case if the observability matrix built from the PMU measurements is poorly conditioned [110] or if the graph has a low connection degree. Building upon the experience of general inverse problems and distributed detection, we propose to design an power grid state estimation using PMUs distributed along the grid. The final goal is to be able to perform early fault detection on the grid. This problem is being tackle with researchers working at INTI (Instituto Nacional de Tecnología Industrial) as part of a larger project. It is worth noting that INTI´s involvement gives particular relevance to this point because of their insertion in the industrial circles in Argentina specifically. It is expected to obtain actual products to be transferred to the users by the end of the project. Activities to be performed (Task 3)

• Design of near optimal detection techniques for distributed sensing on a wireless network: Wireless sensor networks are based on the deployment of large amount of sensor units able to communicate among each other and with a possible fusion center. On that sense, asymptotic performance characterization becomes of practical relevance and can guide the design of detection strategies. In particular, it is interesting to consider the detection of processes that have arbitrary spatial and time correlation. When the correlation is known beforehand, a key issue is how to benefit from this knowledge to save on limited resources (communication channel) without degrading the detection performance (error exponent). When the process may be represented by a Toeplitz

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correlation matrix, then the limiting theory that establishes the diagonalization of the covariance matrix using the Fourier vectors is readily available. The task will be to extend these results to general covariance matrices by considering clustering techniques. In particular, we will propose and analyze the performance of different detection strategies for general processes.

• Grid state estimation for transmission and distribution lines: Early detection of faulty operation of electricity networks has become of uttermost importance to minimize grid outage probability. This can be performed on HV (high voltage) networks using specific PMUs and on MV (medium voltage) or LV (low voltage) networks with smart meters able to measure electric phasor phases. The differences among HV and MV or LV networks are several. Not only these networks respond to different dynamical models, but also the number of sensoring units are of order of magnitude different. On a first instance, we will tackle the problem of detection of faulty behavior for HV systems. In particular, effort will be focused on modelling PMUs dynamics associated with grid outage. Based on this analysis, distributed detection techniques will be proposed to perform early fault detection on these grids. On a second stage, we will analyze MV and LV networks, assuming that the sensoring units are smat meters that provide phase information about the signal being measured.

d) Task #4: Detection of change in boundary conditions The problem of fault detection in a network as described and tackled in task 3 belongs to a class of inverse problems which may be formulated as detecting a change in the boundary conditions of a dynamic system by using some partial and noised observations of its trajectory. This problem is arguably quite general in engineering and may have some degree of relevance in the context of WP1 and WP2. In nature, this framework is relevant for detecting the influence of one or several external radiative forcings (natural or man-induced) on the climate system from the observation at large scale of centennial trends, or of the occurrence at local scale of unusual weather events [111, 112]. This question is of high importance for climate change policy. The present task will focus on implementing the former framework in the latter context based on conceptual tools that will overlap to some extent with other tasks of WP3, and may be of interest for WP1 and WP2. Indeed, mathematically, the latter detection problem can be tackled within the classical framework of state-space models’ estimation. Inference in such models is ubiquitous across many fields; it is implicit to the formulation used in task 4 for data assimilation as well as in the fault detection problem of task 3. For the present application as well as potential others, the unknown boundary condition is assumed to be parameterized with the vector of parameters θ which is to be estimated from the following assumptions:

ttt

ttt

xFyxMx

νεθ

+=

+=+

)()(1 (10)

where the time index t is discrete, xt stands for the unknown state vector, yt is its observed counterpart, M is a deterministic operator describing the system’s dynamic, F is a deterministic observational operator, et and νt are two noise terms. The problem of jointly estimating xt and θ under this setting has received considerable attention over the past decades [113]; it may be solved under closed form equations when the noise is Gaussian and the two operators are linear (Kalman filter [114]) and by augmenting the state vector with the static parameter θ. Otherwise, it may be more generally treated by using Monte-Carlo Bayesian techniques termed particle filtering (or smoothing depending upon the online or offline nature of the application [115]). Algorithms grouped under the term of ensemble Kalman filtering [116] have also been developed in geophysics specifically for large dimensional applications, and may be described as hybrid algorithms in between Kalman filtering and Monte Carlo particle filtering. Activities to be performed (Task 4): We will define formal metrics that are tailored to bringing an answer to the above detection problem, within a data assimilation framework as summarized by Equation (10). In order to do so, we propose to assimilate the same observations into the chosen model M under three different conditions: (i) in an unconstrained way, by allowing the values of all forcings to be tuned to observations; (ii) in a partially constrained way, by artificially imposing no modification of the value of the specific forcing that we wish to evidence; (iii) in a fully constrained way, by artificially imposing no modification of the value of any forcing at all. These three conditions can be represented by three distinct a priori distributions on θ. Then, we will compare the

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degree of realism of the three reconstructions. If (i) is substantially more realistic than (iii), then a change would be detected. If (i) is substantially more realistic than (ii), then a change would be causally attributed to the one forcing that was left unmodified while running (ii) [117]. Conversely, if (i) and (ii) have similar degrees of realism, or the difference in the degree of realism is not significant, then the change cannot be attributed. Within this approach, the key question is thus to design a metric which is able to quantify under an absolute scale the difference in the degree of realism between reconstructions obtained under different conditions – or even better, to quantify the level of evidence associated to the latter difference. We will treat this question as a model selection problem, where our three models correspond to (i), (ii) and (iii). We will then derive a level of evidence by computing the Bayes factor associated to each pair of model. The Bayes factor has the advantage to be interpretable precisely as metric of degree of evidence in favor of a given model, as compared to another model. Its value is interpretable directly on an absolute scale (Jeffrey’s scale). Deriving the Bayes factor numerically in the context of Equation (10) may be a difficult problem both theoretically and computationally, and it will represent a significant part of the present task. The approach will be developed, tested and implemented on the forced version of the Lorenz model [118] which is a simple, low dimensional model that exhibits complex chaotic dynamics, and is often used for the purpose of testing methodological developments in state-space estimation. An interest of this model is that it is able to represent two time scales, the fast one being associated to the system’s own internal variability which is intrinsically chaotic due to the nonlinearity of the Lorenz equations, and the slow one being associated to the progressive change in the forcings, which results in a slow change in the characteristics of the fast and chaotic dynamics, i.e. a slow change in the shape of the trajectory’s attractor in dynamic system’s terminology. Finally, the approach will be applied to a more realistic atmospheric model of intermediate complexity.

II)3)c Team members, staff and research environment

Name Tasks Institution Competences Status

Cecilia G. Galarza 4 CSC-CONICET Distributed signal processing, optimizacion techniques, Communications

Indoor, WP leader, Conicet researcher

Leonardo Rey Vega 4 CSC-CONICET Adaptive signal processing, Information Theory, Communications

Indoor, Conicet Researcher

Juan Augusto Maya 4 FIUBA Distributed signal processing, Large Deviation Theory PhD student

Andrés Altieri 4 FIUBA Information theory, Communication Postdoc

María Inés Troparevsky 1 FUIBA Optimization, Modelling Associate Professor

Francisco Messina 4 FIUBA Signal processing for energy systems , Communications PhD student

Raymundo Albert 4 CSC-CONICET Distributed estimation and detection, signal processing PhD student

Diana Rubio 1 UNSAM Modeling, control systems Associate Professor Marcela Morvidone 1 UNSAM Numerical simulation, optimization Assistant Professor Eduardo P. Serrano 1 UNSAM Nonlinear systems, projection

methods Associate Professor

Silvia Seminara 1 FIUBA Numerical modelling Graduate Teaching Assistant

Javier Cebeiro 1 FIUBA Optimization Graduate Teaching Assistant

Marcela A. Fabio 1 UNSAM Wavelets Assistant Professor Alexis Hannart 2 CNRS Large scale systems CNRS researcher Etienne Mémin 3 INRIA Fluminance Fluid flow dynamics, stochastic

processing INRIA researcher

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Dominique Heitz 3 INRIA Fluminance Fluid dynamics INRIA researcher Cedric Herzet 3 INRIA Fluminance Statistical inference INRIA researcher Guillermo Artana 3 FIUBA -

CONICET Fluid dynamics CONICET researcher

Alejandro Gronskis 3 FIUBA - CONICET Numerical simulation CONICET

researcher Pablo Minninni 3 FCEN – CONICET Image processing, numerical

modeling CONICET researcher

Pablo Piantanida 4 L2S – Supelec - CNRS

Signal processing and information theory Professor

II)3)d Conclusion Solving challenging technological problems require deep knowledge and understanding of large scale systems with complex dynamics. In particular, obtaining realistic solutions to inverse problems for these systems needs adequate utilization of general mathematical tools. The goal of this work package is to develop solutions to specific applied problems using fundamental knowledge from the fields of statistical processing and operator theory. Although there is a large spread of applications to be tacked, the group shows a solid understanding of the fundamental area and it will benefit from the multidisciplinary interaction within WP3 and with the research team responsible for the other two work packages.

III) Organization of the cooperation

III)1 Management Management of the project should be as simple and efficient as possible and should take advantage of the scientific competence present in each organization.

III)1)a Steering committee and management team A steering committee including two French and two Argentinian researchers is proposed to manage the activities of the project, as follows:

France Argentina Person in Charge I Guillaume Haiat (CNRS)

Guillaume.haiat@univ-paris-est.frCecilia Galarza (CONICET) cgalarza@csc.conicet.gov.ar

Person in Charge II Denisse Sciamarella (CNRS) denisse@limsi.fr

Guillermo Artana (CONICET) gartana@fi.uba.ar

It is suggested that a representative of the CNRS INSIS be part of the steering committee for a better adequation of the development of the project with the targets of INSIS. If necessary, we may suggest potential candidates such as for example Yves Rémond, Andrei Constantinescu, Luc Darasse or François Coulouvrat who are also familiar with and contributing to the scientific subject of the project.

III)1)b Scientific committee The role of the scientific committee is to provide some guidelines and recommendations at different stages of the activities of the project. It is composed of external members, from France, Argentina and abroad. Potential external experts are: Prof. Peter Zioupos (Cranfield University), Prof. Shiro Biwa (Kyoto University), Prof. Iwona Jasiuk (University of Illinois), Michael Paidoussis (Mc Gill), Roberto Zenit (UNAM), Prof. Tyrone Vincent (Colorado School of Mines), Pr. Kameshwar Poolla (University of California, Berkley), Pr. Cameron Tropea (Technische Universitaet Darmstadt).

III)1)c Evaluation committee According to the strategy and measure criteria (such as relevance of research fields, scientific originality, active contribution and implementation), indicators (such as researchers mobility, joint publications, participation in international programs), will be followed during the whole duration of the project. Evaluation will be implemented by both national authorities.

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III)2 Cooperation methods

III)2)a Scientific organization In order to provide concrete answers to the needs and objectives described in section I, the joint Franco-Argentinian MoSiME project includes two French CNRS researchers from INSIS who take the commitment of staying in Buenos Aires for the entire duration of the project and three Argentinian CONICET researchers. These five researchers constitute the kernel of the project which also involves other Argentinian and French researchers working in “satellite” teams. The five kernel researchers mentioned above will be permanently based in the Polo Científico (Giol). This organisation guarantees the combination of a large Franco-Argentinian research network with permanent researchers working in GIOL. The list of project participants is given in each workpackage and Fig. 11 represents a schematic description of the organisation of the project including all team members and laboratories from France and Argentina. The “satellite” team members will occasionally come to the CAFCI (around several days up to one week per month) and will realize the associated experiments in their respective laboratories. As described below, MoSiME is centered on modelling and simulation but experimental work will be mandatory in order to validate the models. The network of participants allows for a combination of physical and numerical modelling with experimental techniques. Experiments will be carried out in the satellite laboratories located in France and in Argentina.

Figure 11: Participant and team members of MoSiME. The circle corresponds to the Kernel of the project

and the rectangles to the French and Argentinian laboratories involved.

III)2)b Strategy and relation between the workpackages As described in the introduction, the aim of MoSiME is not to answer collectively to a given and specific scientific question but rather to gather and aggregate in the same laboratory different researchers from diverse background with: _a scientific pertinence and excellence of each researcher in its respective scientific field which are all closely related to the expectation of the call _ a strong willingness of all participants to construct a common laboratory _a strong willingness of all participants to collaborate in the coming years and to start real research projects together by answering to various calls in both countries and in Europe We describe below some potential future links between the workpackages, which have been considered during discussion. Although we believe that these interactions are extremely promising, these interdisciplinary collaborations have only been discussed recently and are not yet mature enough to be detailed scientifically in a contextualized manner. These actions will be done in the beginning of the project. The MoSiME project proposes a consortium of highly competent researchers in their domain and with a

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strong desire to collaborate in the future. Figure 12 summarizes the three WP as well as their interaction. Each WP has a research activity of international level allowing for the development of its international visibility in the perimeter of its research theme for which scientific publication is excellent. Moreover, the skills of the members of the three WP will be put in common in the framework of transversal activities, under the form of strong collaborations between the WP. _Development of model-based inversion procedure in order to retrieve information from the ultrasonic response of the bone-implant interface (WP1-WP3) _Optimal multi-element sensor positioning for the estimation of implant osseointegration (WP1-WP3) _Artificial intelligence approaches to infer bone microstructure from macroscopic measurements (WP1-WP3) _Elasto-dynamic modeling of vocal folds focusing on the influence of the multilayer vocal fold structure on its acoustical response (WP1-WP2) _Development of translational approaches in phonation (WP1-WP2). _Data assimilation and reduced order modelling applied to fluid structure interaction problems (WP2-WP3) Eventually, stochastic simulation approaches represent the “red line” of the project since it is considered in the three WP of the project. All these aforementioned interactions will be possible thanks to the high degree of complementarity between the different team members.

Figure 12: Scientific organization of the project and interaction between the different workpackages

III)2)c Organization of congress and workshops The project is endowed with various tools to establish the cooperation and enhance exchanges of researchers and sharing of knowledge. Workshops are needed to bring together researchers entitled to spend longer periods after these first contacts and also to share a common vision of the problems of interest and their applications to engineering. The interaction between the researchers and scholars will be allowed by the organization of annual workshops. For each workshop, emphasize will be put on a given WP in order to enhance the scientific coherence of the event. All team members and members of satellite laboratories will be invited to the workshop, as well as other French and foreign researchers leaders in their domain. The first seminar will be dedicated to WP3 because it includes methodological aspects which are important for the two other WP. In addition to these annual seminars, smaller monthly seminars will be organized in order to facilitate the scientific communication within the laboratory. The presentation of young researchers will be encouraged.

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The other planned actions are the following: • Topics will be selected during the various stays to propose international Summer Schools open to researchers from and outside the project. • We plan to participate in the organization of one international conference belonging to cycles of existing conferences like for example ICA (International Conference on Acoustics) which will take place in Buenos Aires in 2016. This would be the occasion of gathering and making visible to the international community the results of the cooperation within the MoSiME project.

III)2)d Doctoral training and human resources PhD students co-advised by members of the project will be essential for establishing long time cooperation and producing systematic and deep results of research. The project will promote Master students from France and Argentina on research subjects related to MoSiME. They can then be candidates for joint PhD work. The exchanges of students will contribute to the reinforcement of connections between both countries. The publication of joint papers by the members of MoSiME will punctuate its development during the whole period of the project. They are expected to emerge as the result of intensive discussions and during the Workshops. An important effort will be put towards the training of human resources through the doctoral and post-doctoral stages. In particular, an effort will be put towards fostering co-tutoring PhD programs between the CAFCI and the French satellite. Both French and Argentinian candidates will be considered. All post-doctoral and doctoral students will have to spend time in France and in Argentina as far as practicable. In order to have access to excellent students, the French CNRS researchers will teach in different Master courses, which will allow to attract excellent team members. Exchanges of researchers over periods ranging from one week to three months will be promoted. The idea is to organize the stay of 3 to 5 researchers meeting every day during one to two weeks in Giol and showing the details of their theories and computations on the blackboard or in the computer room. This would promote the elaboration of new models and ideas emerging from deep discussions without time limits and driven by mere curiosity and common endeavour to solve problems. Such situations of quiet and fruitful cooperative work remain too seldom in the current life of researchers.

III)3 Budget This subsection describes the requested budget for the project.

III)3)a Budget for travel and per diem A total budget of 28650€ is requested for the allowance of travel of three Argentinian PhD students or post-doc (one per workpackage) to travel to France during the three years of the project.

YEAR OF IMPLEMENTATION

AIR TICKETS PER DIEM (TOTAL DAYS)

HEALTH INSURANCE

1º 1000€ 3 months = 8100€ 450€ 2º 1000€ 3 months = 8100€ 450€ 3º 1000€ 3 months = 8100€ 450€

Since the priority of MoSiME is the exchange of young researcher, the entire budget will be devoted to young Argentinian researchers only (PhD students and post-doc). The requested budget corresponds to a 3 month stay in France for each person. A three months stay is considered in order to allow a sufficient amount of time for fruitful collaboration in France. Each workpackage will have the opportunity to use this funding based on equality distribution. Moreover, we will look for additional funding opportunities to increase the number of exchange of young researchers between France and Argentina such as for example the Houssay Program, the Ecos Sud program

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and the Bec.ar program, which allows to fund long term exchanges of PhD students at the end of their PhD study.

III)3)b Budget for consumable, workshop and third party services YEAR CONSUMABLES AND

MINOR EQUIPMENT WORKSHOPS

ORGANIZATION THIRD PARTY SERVICES

JUSTIFICATION

1º 12000 10000 3000 See below 2º 12000 10000 3000 See below 3º 12000 10000 3000 See below

Each workpackage will have the opportunity to use this funding based on equality distribution. The Table below summarizes the needs according to the WP for Consumables and minor equipment and third party services.

WP1 WP2 WP3 Consumable and minor

equipement Labtop, PC computer, hard disk, Implants,

Supplies and tools for the preparation of the

specimens

Laptop, PC computer, hard disks for data

storage, lenses, Supplies and tools for the

preparation of models.

Labtop, PC computer, function generator

Third party services Implant coating, machining of pieces on

demand, network.

Machining of pieces on demand.

Network technician, software.

An important budget is requested for workshop organization in order to be able to invite French (but also sometimes foreign) researchers to Argentina, which will be beneficial for the project. The requested budget will cover the invitation of two researchers to the workshop, thus covering the plane ticket and living expenses for one week. Moreover, coffee break and one restaurant will be organized.

III)3)c Budget for travel and per diem within Argentina Due to the strong collaboration with Mar del Plata (WP1), the budget below is requested for traveling between Mar del Plata and Buenos Aires for the team in UNMP (5 stays of one week per year) and for the travel of Guillaume Haiat to Mar del Plata (3 stays of one week per year).

YEAR OF

IMPLEMENTATION TRAVEL EXPENSES

(INCLUDING ITINERARY) LIVING EXPENSES JUSTIFICATION

1º 1600€ 2000€ See below 2º 1600€ 2000€ See below 3º 1600€ 2000€ See below

IV) Conclusion

IV)1 Expected added value The MoSiME project represents a unique combination of theoretical and computational researchers to address topical research subjects that are currently handled in a much too isolated way. Mutual stimulation, interdisciplinarity and industrial collaboration is expected to lead to highly visible advances in this field. The complementarity of the project members has been illustrated several times in the past. The structure of the MoSiME project composed by a kernel of 5 researchers (2 CNRS and 3 CONCET) and of 15 “satellite laboratories” will allow strong synergy and fruitful collaboration between France and Argentina. The UMI label will enhance the national and international recognition of the actions of the MoSiME project and of the satellite laboratories. It will draw the attention of Universities, Research Institutes on the chosen research topics and encourage attribution of funding from the ANR and Horizon 2020. Above all, we expect that young talented researchers from both sides will strongly benefit from this incentive to cooperate on topical subjects, earning recognition and possible funding for their commitment.

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IV)2 Long term development of the MoSiME project Installing and reinforcing a cooperation tradition between France and Argentina in the field of engineering will facilitate the development of (i) regular joint doctoral courses emanating from the various Schools planned in the project and (ii) joint master courses or even joint master program. Doctoral courses will become essential parts of European doctor programs in the near future. The structure of doctoral programs is building up in Paris, for instance in Paris-Saclay and in Paris-Est and we will have time to see how the contribution of the project can be officially implemented. The international dimension of courses is important for doctoral students. We think that the MoSiME project can contribute to promote courses in English in Argentina based on the scientific subjects of the project, thus attracting master students towards challenging scientific problems to be tackled during a PhD. Beside ANR and funding dedicated to the Franco-Argentinian cooperation (like for example ECOS-Sud or the Houssay program), another target is Horizon 2020, the new European project platform, Argentina being considered as an associated country. It is anticipated that the stays of researcher in GIOL will also be used to prepare joint projects with other European partners. The targeted projects are Marie Sklodowska Curie networks but also engineering projects in cooperation with industrial partners. We can also help our young outstanding researchers to prepare ERC starting grant projects.

References [1] J. Wolff, The Law of Bone Remodeling. Berlin: Springer, 1986. [2] C. C. Swan, R. S. Lakes, R. A. Brand, and K. J. Stewart, "Micromechanically based poroelastic

modeling of fluid flow in Haversian bone," J Biomech Eng., vol. 125, pp. 25-37., 2003. [3] W. Winter, S. M. Heckmann, and H. P. Weber, "A time-dependent healing function for immediate

loaded implants," J Biomech., vol. 37, pp. 1861-7., 2004. [4] S. Ilic, K. Hackl, and R. Gilbert, "Application of the multiscale FEM to the modeling of cancellous

bone," Biomech Model Mechanobiol, vol. 9, pp. 87-102, 2009. [5] L. Podshivalov, A. Fischer, and P. Z. Bar-Yoseph, "3D hierarchical geometric modeling and

multiscale FE analysis as a base for individualized medical diagnosis of bone structure," Bone., vol. 48, pp. 693-703, 2010.

[6] I. Jager and P. Fratzl, "Mineralized collagen fibrils: a mechanical model with a staggered arrangement of mineral particles," Biophys J., vol. 79, pp. 1737-46., 2000.

[7] M. L. Knothe Tate, "Top down and bottom up engineering of bone," J Biomech., vol. 44, pp. 304-12, 2010.

[8] J. A. Izaguirre, R. Chaturvedi, C. Huang, T. Cickovski, J. Coffland, G. Thomas, G. Forgacs, M. Alber, G. Hentschel, S. A. Newman, and J. A. Glazier, "CompuCell, a multi-model framework for simulation of morphogenesis," Bioinformatics., vol. 20, pp. 1129-37. , 2004.

[9] S. Vesentini, C. F. Fitie, F. M. Montevecchi, and A. Redaelli, "Molecular assessment of the elastic properties of collagen-like homotrimer sequences," Biomech Model Mechanobiol., vol. 3, pp. 224-34., 2005.

[10] C. Hellmich, J. Barthelemy, and L. Dormieux, "Mineral–collagen interactions in elasticity of bone ultrastructure – a continuum micromechanics approach," Eur. J. Mech., A Solids, vol. 23, pp. 783–810, 2004.

[11] C. Hellmich and F. J. Ulm, "Micromechanical Model for Ultrastructural Stiffness of Mineralized Tissues," J Eng Mech, vol. 128, pp. 898-908, 2002.

[12] S. Checa and P. J. Prendergast, "A mechanobiological model for tissue differentiation that includes angiogenesis: a lattice-based modeling approach," Ann Biomed Eng., vol. 37, pp. 129-45. , 2009.

[13] G. Haiat, F. Padilla, M. Svrcekova, Y. Chevalier, D. Pahr, F. Peyrin, P. Laugier, and P. Zysset, "Relationship between ultrasonic parameters and apparent trabecular bone elastic modulus: a numerical approach," J Biomech., vol. 42, pp. 2033-9., 2009.

[14] H. H. Bayraktar, E. F. Morgan, G. L. Niebur, G. E. Morris, E. K. Wong, and T. M. Keaveny, "Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue," J Biomech., vol. 37, pp. 27-35., 2004.

[15] Z. Yosibash, R. Padan, L. Joskowicz, and C. Milgrom, "A CT-based high-order finite element analysis of the human proximal femur compared to in-vitro experiments," J Biomech Eng., vol. 129, pp. 297-309., 2007.

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42

[16] V. Sansalone, V. Bousson, S. Naili, C. Bergot, F. Peyrin, J. D. Laredo, and G. Haiat, "Anatomical distribution of the degree of mineralization of bone tissue in human femoral neck: impact on biomechanical properties," Bone., vol. 50, pp. 876-84, 2012.

[17] V. Sansalone, S. Naili, V. Bousson, C. Bergot, F. Peyrin, J. Zarka, J. D. Laredo, and G. Haiat, "Determination of the heterogeneous anisotropic elastic properties of human femoral bone: from nanoscopic to organ scale," J Biomech, vol. 43, pp. 1857-63, 2010.

[18] J. Olive and C. Aparicio, "Periotest method as a measure of osseointegrated oral implant stability," Int J Oral Maxillofac Implants., vol. 5, pp. 390-400., 1990.

[19] N. Meredith, D. Alleyne, and P. Cawley, "Quantitative determination of the stability of the implant-tissue interface using resonance frequency analysis," Clin Oral Implants Res., vol. 7, pp. 261-7., 1996.

[20] V. Pattijn, C. Van Lierde, G. Van der Perre, I. Naert, and J. Vander Sloten, "The resonance frequencies and mode shapes of dental implants: Rigid body behaviour versus bending behaviour. A numerical approach," J Biomech., vol. 39, pp. 939-47., 2006.

[21] C. Aparicio, N. P. Lang, and B. Rangert, "Validity and clinical significance of biomechanical testing of implant/bone interface," Clin Oral Implants Res., vol. 17, pp. 2-7., 2006.

[22] R. Swain, G. Faulkner, D. Raboud, and J. Wolfaardt, "An improved impact technique for monitoring percutaneous implant integrity," Int J Oral Maxillofac Implants., vol. 23, pp. 263-9., 2008.

[23] V. Mathieu, K. Fukui, M. Matsukawa, M. Kawabe, R. Vayron, E. Soffer, F. Anagnostou, and G. Haiat, "Micro-Brillouin Scattering Measurements in Mature and Newly Formed Bone Tissue Surrounding an Implant," J Biomech Eng., vol. Vol. 133, 2011.

[24] V. Mathieu, F. Anagnostou, E. Soffer, and G. Haiat, "Numerical simulation of ultrasonic wave propagation for the evaluation of dental implant biomechanical stability," J Acoust Soc Am, vol. 129, pp. 4062-72, 2011.

[25] V. Mathieu, F. Anagnostou, E. Soffer, and G. Haiat, "Ultrasonic evaluation of dental implant biomechanical stability: an in vitro study," Ultrasound Med Biol, vol. 37, pp. 262-70., 2011.

[26] M. Viceconti, R. Muccini, M. Bernakiewicz, M. Baleani, and L. Cristofolini, "Large-sliding contact elements accurately predict levels of bone-implant micromotion relevant to osseointegration," J Biomech., vol. 33, pp. 1611-8., 2000.

[27] U. Andreaus and M. Colloca, "Prediction of micromotion initiation of an implanted femur under physiological loads and constraints using the finite element method," Proc Inst Mech Eng H., vol. 223, pp. 589-605., 2009.

[28] S. Gupta, B. Pal, and A. M. New, "The effects of interfacial conditions and stem length on potential failure mechanisms in the uncemented resurfaced femur," Ann, vol. 38, pp. 2107-20. , 2010.

[29] C. Avenel, E. Mémin, and P. Pérez, "Stochastic level set dynamics to track closed curves through image data," J. of Math. Imaging and Vision, pp. s10851, 2013.

[30] J. Folgado, P. R. Fernandes, C. R. Jacobs, and V. D. Pellegrini, "Influence of femoral stem geometry, material and extent of porous coating on bone ingrowth and atrophy in cementless total hip arthroplasty: an iterative finite element model," Comput Methods Biomech Biomed Engin, vol. 12, pp. 135-145, 2009.

[31] C. Rungsiyakull, Q. Li, G. Sun, W. Li, and M. V. Swain, "Surface morphology optimization for osseointegration of coated implants," Biomaterials., vol. 31, pp. 7196, 2010.

[32] M. T. Bah, P. B. Nair, and M. Browne, "Mesh morphing for finite element analysis of implant positioning in cementless total hip replacements," Med Eng Phys., vol. 31, pp. 1235-43., 2009.

[33] U. Simon, P. Augat, A. Ignatius, and L. Claes, "Influence of the stiffness of bone defect implants on the mechanical conditions at the interface--a finite element analysis with contact," J Biomech., vol. 36, pp. 1079-86., 2003.

[34] M. T. Bah, P. B. Nair, M. Taylor, and M. Browne, "Efficient computational method for assessing the effects of implant positioning in cementless total hip replacements," J Biomech, vol. 44, pp. 1417-1422, 2011.

[35] M. Viceconti, G. Brusi, A. Pancanti, and L. Cristofolini, "Primary stability of an anatomical cementless hip stem: a statistical analysis," J Biomech., vol. 39, pp. 1169-79., 2006.

[36] P. J. Laz, S. Pal, J. P. Halloran, A. J. Petrella, and P. J. Rullkoetter, "Probabilistic finite element prediction of knee wear simulator mechanics," J Biomech., vol. 39, pp. 2303-10., 2006.

[37] Y. T. Sul, C. B. Johansson, Y. Jeong, K. Roser, A. Wennerberg, and T. Albrektsson, "Oxidized implants and their influence on the bone response," J Mater Sci Mater Med., vol. 12, pp. 1025-31., 2001.

MoSiME CAFCI

43

[38] K. Gotfredsen, T. Berglundh, and J. Lindhe, "Bone reactions adjacent to titanium implants subjected to static load of different duration. A study in the dog (III)," Clin Oral Implants Res., vol. 12, pp. 552-8., 2001.

[39] J. Duyck, H. J. Ronold, H. Van Oosterwyck, I. Naert, J. Vander Sloten, and J. E. Ellingsen, "The influence of static and dynamic loading on marginal bone reactions around osseointegrated implants: an animal experimental study," Clin Oral Implants Res., vol. 12, pp. 207-18., 2001.

[40] H. J. Ronold and J. E. Ellingsen, "The use of a coin shaped implant for direct in situ measurement of attachment strength for osseointegrating biomaterial surfaces," Biomaterials., vol. 23, pp. 2201-9., 2002.

[41] H. J. Ronold and J. E. Ellingsen, "Effect of micro-roughness produced by TiO2 blasting--tensile testing of bone attachment by using coin-shaped implants," Biomaterials., vol. 23, pp. 4211-9., 2002.

[42] V. Mathieu, R. Vayron, E. Barthel, D. Dalmas, J. E. Soffer, F. Anagnostou, and G. Haïat, "Mode III cleavage of a coin-shaped titanium implant in bone: effect of friction and crack propagation," J Mech Behav Biomed Mater, vol. 8, pp. 194-203, 2012.

[43] V. Mathieu, J. E. Soffer, F. Anagnostou, and G. Haïat, "Influence of healing time on the ultrasonic response of the bone-implant interface," Ultrasound Med Biol, vol. 38, pp. 611-618, 2012.

[44] R. Vayron, E. Barthel, V. Mathieu, J. E. Soffer, F. Anagnostou, and G. Haïat, "Nanoindentation Measurements of Biomechanical Properties in Mature and Newly Formed Bone Tissue Surrounding an Implant," J Biomech Eng., vol. 134, pp. 021007, 2012.

[45] G. Haiat, S. Naili, M. Ba Vu, C. Desceliers, and C. Soize, "Equivalent contributing depth investigated by a lateral wave with axial transmission in viscoelastic cortical bone," J Acoust Soc Am., vol. 129, pp. EL114-20, 2011.

[46] J. C. Michel, H. Moulinec, and P. Suquet, "A computational scheme for linear and non linear composites with arbitrary phase contrast," International Journal for Numerical Methods in Engineering, vol. 52, pp. 139-160, 2001.

[47] J. Sierpowska, M. J. Lammi, M. A. Hakulinen, J. S. Jurvelin, R. Lappalainen, and J. Töyräs, "Effect of human trabecular bone composition on its electrical properties.," Medical engineering & physics, vol. 29, pp. 845-852, 2007.

[48] R. M. Irastorza, E. Blangino, C. M. Carlevaro, and F. Vericat, "Modelling of the dielectric properties of trabecular bone samples at microwave frequency," Medical & Biological Engineering & Computing, 2013.

[49] G. Haiat and E. Barthel, "An approximate model for the adhesive contact of rough viscoelastic surfaces," Langmuir, vol. 23, pp. 11643, 2007.

[50] G. Haiat, A. Lhemery, P. Calmon, and F. Lasserre, "A model-based inverse method for positioning scatterers in a cladded component inspected by ultrasonic waves," Ultrasonics., vol. 43, pp. 619-28. Epub 2005 Apr 18., 2005.

[51] G. Haiat, S. Naili, Q. Grimal, M. Talmant, C. Desceliers, and C. Soize, "Influence of a gradient of material properties on ultrasonic wave propagation in cortical bone: application to axial transmission," J Acoust Soc Am., vol. 125, pp. 4043-52., 2009.

[52] G. Haiat, F. Padilla, R. Barkmann, C. C. Gluer, and P. Laugier, "Numerical simulation of the dependence of quantitative ultrasonic parameters on trabecular bone microarchitecture and elastic constants," Ultrasonics., vol. 44S, pp. e289-e294, 2006.

[53] G. Haiat, F. Padilla, F. Peyrin, and P. Laugier, "Variation of ultrasonic parameters with microstructure and material properties of trabecular bone: a 3D model simulation," J Bone Miner Res., vol. 22, pp. 665-74., 2007.

[54] V. Sansalone, S. Naili, V. Bousson, C. Bergot, F. Peyrin, J. Zarka, J. D. Laredo, and G. Haiat, "Determination of the heterogeneous anisotropic elastic properties of human femoral bone: From nanoscopic to organ scale," J Biomech, vol. 2010, pp. 12, 2010.

[55] V. Mathieu, A. Michel, C. Flouzat-Lachaniette, A. Poignard, P. Hernigou, J. Allain, and G. Haïat, "Variation of the impact duration during the in vitro insertion of acetabular cup implant," Med Eng Phys, vol. 35, pp. 1558-1563, 2013.

[56] V. Mathieu, F. Anagnostou, J. Soffer, and G. Haiat, "Ultrasonic evaluation of dental implant biomechanical stability: an in vitro study," Ultrasound Med Biol, vol. 37, pp. 262-270, 2011.

[57] R. Vayron, P. Karasinski, V. Mathieu, A. Michel, D. Loriot, G. Richard, G. Lambert, and G. Haïat, "Variation of the ultrasonic response of a dental implant embedded in tricalcium silicate-based cement under cyclic loading," J Biomech, vol. 46, pp. 1162–1168, 2013.

MoSiME CAFCI

44

[58] G. Haiat, A. Lhémery, F. Renaud, F. Padilla, P. Laugier, and S. Naili, "Velocity dispersion in trabecular bone: influence of multiple scattering and of absorption," J Acoust Soc Am, vol. 124, pp. 4043-4052, 2008.

[59] V. Mathieu, R. Vayron, G. Richard, G. Lambert, S. Naili, J.-P. Meningaud, and G. Haiat, "Biomechanical determinants of the stability of dental implants: influence of the bone-implant interface properties," J Biomech, vol. 47, pp. 3-13, in press.

[60] G. Haiat, H. L. Wang, and J. B. Brunski, "Effects of biomechanical properties of the bone-implant interface on dental implant stability: from in silico approaches to the patient’s mouth," Annu Rev Biomed Eng. , in press.

[61] A. Gomez Sanchez, W. H. Schreiner, J. Ballarre, A. Cisilino, G. Duffo, and S. Cere, "Surface modification of titanium by anodic oxidation in phosphoric acid at low potentials. Part 2. In vitro and in vivo study," Surface and Interface Analysis, vol. 45, pp. 1395-1401, 2013.

[62] J. Ballarre, R. Seltzer, E. Mendoza, J. Orellano, Y. Mai, C. Garcia, and S. Cere, "Morphologic and nanomechanical characterization of bone tissue growth around bioactive sol–gel coatings containing wollastonite particles applied on stainless steel implants," Materials Science and Engineering C 31, vol. 31, pp. 545-552, 2011.

[63] J. Ballarre, I. Manjubala, W. H. Schreiner, J. C. Orellano, P. Fratzl, and S. Cere, "Improving the osteointegration and bone-implant interface by incorporation of bioactive particles in sol-gel coatings of stainless steel implants," Acta Biomater, vol. 6, pp. 1601-9, 2010.

[64] A. Gomez Sanchez, J. Ballarre, J. C. Orellano, G. Duffo, and S. Cere, "Surface modification of zirconium by anodisation as material for permanent implants: in vitro and in vivo study," J Mater Sci Mater Med, vol. 24, pp. 161-9, 2012.

[65] L. E. Ritacco, F. E. Milano, G. L. Farfalli, M. A. Ayerza, D. L. Muscolo, and L. A. Aponte-Tinao, "Accuracy of 3-D planning and navigation in bone tumor resection," Orthopedics., vol. 36, pp. e942-50. , 2013.

[66] L. E. Ritacco, G. L. Farfalli, F. E. Milano, M. A. Ayerza, D. L. Muscolo, and L. Aponte-Tinao, "Three-dimensional virtual bone bank system workflow for structural bone allograft selection: a technical report," Sarcoma, vol. 2013, pp. 9, 2013.

[67] L. A. Aponte-Tinao, L. E. Ritacco, M. A. Ayerza, D. L. Muscolo, and G. L. Farfalli, "Multiplanar osteotomies guided by navigation in chondrosarcoma of the knee," Orthopedics., vol. 36, pp. e325-30., 2013.

[68] S. Naili, M. B. Vu, Q. Grimal, M. Talmant, C. Desceliers, C. Soize, and G. Haiat, "Influence of viscoelastic and viscous absorption on ultrasonic wave propagation in cortical bone: Application to axial transmission," J Acoust Soc Am, vol. 127, pp. 2622-34., 2010.

[69] F. Krebs, F. Silva, D. Sciamarella, and G. Artana, "A three-dimensional study of the glottal jet. ," Experiments in Fluids, vol. 52, pp. 1133-1147, 2011.

[70] D. Sciamarella, F. Silva , and G. Artana, "Similarity analysis of a glottal-like jet," Experiments in Fluids, vol. 53, pp. 765-776, 2012.

[71] D. Sciamarella and P. L. Quere, "Solving for unsteady airflow in a glottal model with immersed moving boundaries.," European Journal of Mechanics-B/Fluids, vol. 27, pp. 42-53, 2008.

[72] E. J. Gutmark and F. F. Grinstein, "Flow control with noncircular jets," Ann. Rev. Fluid Mech., vol. 31, pp. 239-272, 1999.

[73] D. Sciamarella, A. Centeno, and G. Artana, "Bifurcation of a bio-inspired elongated jet," Experiments in Fluids, submitted.

[74] X. Zheng, R. Mittal, Q. Xue, and S. Bielamowicz, "Direct-Numerical Simulation of the Glottal Jet and Vocal-Fold Dynamics in a Three-Dimensional Laryngeal Model," J. Acoustical Society of America, vol. 130, pp. 404-415, 2011.

[75] L. Monasse, V. Daru, C. Mariotti, S. Piperno, and C. Tenaud, "A conservative coupling algorithm between a compressible flow and a rigid body using an Embedded Boundary method," Journal of Computational Physics, 2012.

[76] I. R. Titze, Principles of voice production, 2000. [77] V. Daru, P. Le Quéré, M.-C. Duluc, and O. Le Maître, "A numerical method for the simulation of

low Mach number liquid–gas flows," Journal of Computational Physics, 2010. [78] L. I. Lago, F. L. Ponta, and A. D. Otero., " Analysis of alternative adaptive geometrical

configurations for the NREL-5MW wind turbine blade," Renewable Energy, pp. 0960–1481, 2013. [79] A. D. Otero and F. L. Ponta, "Structural analysis of wind-turbine blades by a generalized

Timoshenko beam model," Journal of Solar Energy Engineering, vol. 132, pp. 011015, 2010.

MoSiME CAFCI

45

[80] F. S. Gagliano, D. , A. D. Otero, and R. Sosa, "Ensayo de rotores eólicos en pequeña escala," presented at En Anales III CAIM 2012, 2012.

[81] D. Schupbach, F. Gagliano, A. D. Otero, and R. Sosa, "Construcción y validación de un equipo para ensayo de rotores eólicos en pequeña escala," En Avances en Energías Renovables y Medio Ambiente, vol. 15, pp. 06.15-06.22, 2011.

[82] J. M. Cabaleiro and J.-L. Aider, "Axis-switching of a micro-jet," Physics of Fluids, vol. 031702 2014.

[83] M. J. Rodriguez, I. Iscla, and L. Szczupak, "Modulation of mechanosensory responses by motoneurons that regulate skin surface topology in the leech," J. Neurophisiol., vol. 91, pp. 2366-2375, 2004.

[84] J. O. Dabiri, "Optimal vortex formation as a unifying principle in biological propulsion," Annual Review of Fluid Mechanics, vol. 41, pp. 1733, 2009.

[85] H. T. Banks, S. Dediu, and S. L. Ernstberger, "Sensitivity functions and their uses in inverse problems," J. Inverse and Ill-posed Problems, vol. 15, pp. 683-708, 2007.

[86] H. T. Banks, S. Dediu, S. L. Ernstberger, and F. Kappel, "A new optimal approach to optimal design problem," J. inverse and ill-posed problems, vol. 18, pp. 25-83, 2010.

[87] H. T. Banks, K. Holm, and F. Kappel, "Comparison of optimal design methods in inverse problems," Inverse Problems, vol. 27, pp. 075002, 2011.

[88] G. A. F. Seber and C. J. Wild, Nonlinear Regression. Hoboken, NJ,, 2003. [89] H. T. Banks, S. Hu, and W. C. Thompson, Modeling and Inverse Problems in the Presence of

Uncertainty. Boca Raton, FL, in press. [90] G. Artana, A. Cammilleri, J. Carlier, and E. Mémin, "Strong and weak constraint variational

assimilations for reduced order fluid flow modeling," Int J. of Comp. Phy, vol. 213, pp. 3264-3288, 2012.

[91] S. Beyou, A. Cuzol, S. Gorthi, and E. Mémin, "Weighted Ensemble Transform Kalman Filter for Image Assimilation. ," Tellus A, vol. 65, pp. 18803, 2013.

[92] T. Corpetti, P. Héas, E. Mémin, and N. Papadakis, "Pressure image assimilation for atmospheric motion estimation. ," Tellus, vol. 61, pp. 160-178, 2009.

[93] E. Mémin, "Fluid flow dynamics under location uncertainty," Geophysical & Astrophysical Fluid Dynamics, in press.

[94] A. J. Majda, I. Timofeyev, and E. Vanden Eijnden, "Models for stochastic climate prediction," Proc Natl Acad Sci U S A, vol. 96, pp. 14687-91, 1999.

[95] G. Haller, "Lagrangian coherent structures and the rate of strain in two-dimensional turbulence," Phys. Fluids, vol. 13, pp. 3365-3385, 2001.

[96] I. Mezic, S. Loire, V. A. Fonoberov, and P. Hogan, "A new mixing diagnostic and Gulf oil spill movement," Science, vol. 330, pp. 486-9, 2010.

[97] A. Swami, Q. Zhao, Y. Hong, and L. Tong, Wireless Sensor Networks. Signal Processing and Communications Perspectives, 2007.

[98] J. A. Maya, L. Rey Vega, and C. G. Galarza, "Error exponents for bias detection of a correlated process over a MAC fading channel," presented at IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013.

[99] J. A. Maya, L. Rey Vega, and C. G. Galarza, "Distributed Detection of Correlated Random Processes with Energy and Bandwidth Constraints," presented at Sensor Array and Multichannel Signal Processing Workshop (SAM), IEEE, 2011.

[100] W. Li and H. Dai, "Distributed Detection in Wireless Sensor Networks Using A Multiple Access Channel," IEEE Trans. Signal Process, vol. 55, pp. 822-833, 2007.

[101] A. Giridhar and P. Kumar, "Computing and Communicating Functions Over Sensor Networks," IEEE Journal on Selected Areas in Communications, vol. 23, pp. 755-764, 2005.

[102] A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications. New York, 1998. [103] J. Chamberland and V. Veeravalli, "How Dense Should a Sensor Network be for Detection with

Correlated Observations," IEEE Transactions on Information Theory, vol. 52, pp. 5099-5107, 2006. [104] K. Cohen and A. Leshem, "Energy-Efficient Detection in Wireless Sensor Networks Using

Likelihood Ratios and Channel State Information," IEEE Journal on Selected Areas in Communications, vol. 29, pp. 1671-1684, 2011.

[105] G. Valverde and V. Terzija, "Unscented Kalman filter for power system dynamic state estimation," IET Generation, Transmission Distribution, pp. 29-37, 2011.

[106] L. Fan, Z. Miao, and Y. Wehbe, "Application of Dynamic State and Parameter Estimation Techniques on Real-World Data," IEEE Transactions on Smart Grid, pp. 1133-1141, 2013.

MoSiME CAFCI

46

[107] F. Aminifar, M. Shahid, M. Fotuhi, and S. Kamali, "Power System Dynamic State Estimation With Synchronized Phasor Measurements," Instrumentation and Measurement, IEEE Trans. on, vol. 63, pp. 352,363, 2014.

[108] A. Aburand and A. Gómez-Expósito, Power System State Estimation: Theory and Implementation, 2004.

[109] A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Statistics and Computing, vol. 10, pp. 197-208, 2000.

[110] A. Monticelli and F. F. Wu, "Network observability: Theory," IEEE Trans. Power App. Syst., vol. 104, pp. 10421048, 1985.

[111] P. A. e. a. Stott, Climate Science for Serving Society: Research, Modeling and Prediction Priorities: Springer, 2013.

[112] G. C. Hegerl and F. W. Zwiers, "Use of models in detection and attribution of climate change," WIREs Climate Change, vol. 2, pp. 570-591, 2011.

[113] N. e. a. Kantas, "An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models," presented at Proceedings IFAC System Identification Meeting, 2009.

[114] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, vol. 82, pp. 35-45, 1960.

[115] M. S. e. a. Arulampalam, "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE Transactions on Signal Processing, vol. 50, pp. 174-188, 2002.

[116] G. Evensen, "Sequential data assimilation with nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics," Journal of Geophysical Research, vol. 99, pp. 143-162, 1994.

[117] J. Pearl, Causality: models, reasoning and inference: Cambridge University Press, 2000. [118] E. N. Lorenz, "Deterministic non periodic flow," Journal of the Atmospheric Sciences, vol. 20, pp.

130-141, 1963.